{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/timeseriesAI/tsai/blob/master/tutorial_nbs/08_Self_Supervised_MVP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "created by Ignacio Oguiza - email: timeseriesAI@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MVP (previously TSBERT): Self-Supervised Pretraining of Time Series Models 🤗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an unofficial PyTorch implementation created by Ignacio Oguiza (timeseriesAI@gmail.com) based on:\n",
    "\n",
    "* Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., & Eickhoff, C. (2020). A Transformer-based Framework for Multivariate Time Series Representation Learning. arXiv preprint arXiv:2010.02803v2.. No official implementation available as far as I know (Oct 10th, 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`MVP` is a self-supervised training method that can be used to pretrain time series models without using any labels. The approach is very similar to BERT.\n",
    "\n",
    "`MVP` is performed in 2 steps: \n",
    "\n",
    "1. Pretrain the selected architecture without any labels. When training is finished, the pretrained model will be automatically saved to the given target_dir/fname.\n",
    "2. Fine-tune or train the same architecture with pretrained=True indicating the weights_path (target_dir/fname).\n",
    "\n",
    "\n",
    "In this notebook we'll use a UCR dataset (LSST) that contains around 2500 training and 2500 validation samples. To analyze the impact of `MVP` we'll:\n",
    "1. use supervised learning to set a baseline using 10% or 100% of the labels.\n",
    "2. pretrain a model using 100% of the training dataset without labels.\n",
    "3. fine tune or train using 10% or 100% of the training dataset (with labels). \n",
    "\n",
    "A key difference between `MVP` and the original paper is that you can use any architecture of your choice as long as it has a \"head\" attribute and can take a custom_head kwarg. Architectures finished in Plus in the `tsai` library meet this criteria. To demonstrate how this works, we'll use InceptionTimePlus throughout this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/timeseriesAI/tsai/blob/master/tutorial_nbs/images/TSBERT_data.jpg?raw=1\">\n",
    "<img src=\"https://github.com/timeseriesAI/tsai/blob/master/tutorial_nbs/images/TSBERT_chart.jpg?raw=1\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results indicate the following: \n",
    "\n",
    "* Pretraining + fine-tuning/ training improves performance when compared to supervised learning (training from scratch).\n",
    "* In this case, there's not much difference between fine-tuning and training a pretrained model. This may be dataset dependent. It'd be good to try both approaches. \n",
    "* The fewer labels available, the better pretraining seems to work. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries 📚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## NOTE: UNCOMMENT AND RUN THIS CELL IF YOU NEED TO INSTALL/ UPGRADE TSAI\n",
    "# stable = False # True: stable version in pip, False: latest version from github\n",
    "# if stable: \n",
    "#     !pip install tsai -U >> /dev/null\n",
    "# else:      \n",
    "#     !pip install git+https://github.com/timeseriesAI/tsai.git -U >> /dev/null\n",
    "# ## NOTE: REMEMBER TO RESTART (NOT RECONNECT/ RESET) THE KERNEL/ RUNTIME ONCE THE INSTALLATION IS FINISHED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K     |████████████████████████████████| 143kB 13.7MB/s \n",
      "\u001b[K     |████████████████████████████████| 3.2MB 23.0MB/s \n",
      "\u001b[K     |████████████████████████████████| 5.7MB 22.5MB/s \n",
      "\u001b[K     |████████████████████████████████| 194kB 58.4MB/s \n",
      "\u001b[K     |████████████████████████████████| 9.5MB 59.1MB/s \n",
      "\u001b[K     |████████████████████████████████| 92kB 10.9MB/s \n",
      "\u001b[K     |████████████████████████████████| 2.5MB 49.7MB/s \n",
      "\u001b[K     |████████████████████████████████| 296kB 54.4MB/s \n",
      "\u001b[K     |████████████████████████████████| 22.2MB 1.7MB/s \n",
      "\u001b[K     |████████████████████████████████| 174kB 64.6MB/s \n",
      "\u001b[K     |████████████████████████████████| 25.3MB 1.3MB/s \n",
      "\u001b[K     |████████████████████████████████| 61kB 8.3MB/s \n",
      "\u001b[K     |████████████████████████████████| 675kB 57.5MB/s \n",
      "\u001b[K     |████████████████████████████████| 102kB 9.0MB/s \n",
      "\u001b[K     |████████████████████████████████| 102kB 12.2MB/s \n",
      "\u001b[?25h  Building wheel for contextvars (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[31mERROR: distributed 2021.1.1 has requirement dask>=2020.12.0, but you'll have dask 2.12.0 which is incompatible.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n",
      "  warnings.warn(problem)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tsai       : 0.2.14\n",
      "fastai     : 2.2.5\n",
      "fastcore   : 1.3.19\n",
      "torch      : 1.7.0+cu101\n"
     ]
    }
   ],
   "source": [
    "from tsai.all import *\n",
    "computer_setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data 🏭"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll first import the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsid = 'LSST'\n",
    "X, y, splits = get_UCR_data(dsid, split_data=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now create 2 dataloaders with 100% of the training and 100% of validation samples.\n",
    "One of them doesn't contain the y (unlabeled). The other one contains the labels. \n",
    "We'll use the unlabeled dataset (udls) to pretrain the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X      - shape: [4925 samples x 6 features x 36 timesteps]  type: memmap  dtype:float32  isnan: 0\n",
      "y      - shape: (4925,)  type: memmap  dtype:<U2  n_classes: 14 (351 samples per class) ['15', '16', '42', '52', '53', '6', '62', '64', '65', '67', '88', '90', '92', '95']  isnan: False\n",
      "splits - n_splits: 2 shape: [2459, 2466]  overlap: [False]\n"
     ]
    },
    {
     "data": {
      "image/png": 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fxZwGcqKqqqpW0sW5zqM7yGZV5A1JJySrqh6m9IIoz2UxPtDZnpPUvAL7tZL+K6P9mmQV99MlfZZcmv2CpCm2j0oWZ5uStAFdLrnn9FFJ6yPinzJ2Ma/RY9kuSa4UkO3+kv5E6fUzlku6LOnWcl43z/fLJL0UEZG0z0hWeD9e6UU3X++aowB+LyJ+EhGlEVGm9GfllyJippjTAHIsa1cMRETK9l8o/QGyUNK8iHgvW/GBbLK9UOlvRYfY3q70Kuz3SXrS9vWSPpQ0Pem+VNIFSi/ss1fSjyQpIupt36t0UUyS5kREywUNga7yXUlXS3onuR9bkv5azGv0bMdIWpCstl4g6cmIWGJ7naQnbM+V9Fuli2JK/vwP21uUXmB2hiRFxHu2n5S0TukneNyU3KIAdBezxZwGkENOFx0BAAAAAEA+6s0LLAAAAAAAgA5QGAAAAAAAII9RGAAAAAAAII9RGAAAAAAAII9RGAAAAAAAII9RGAAAAAAAII9RGAAAAAAAII9RGAAAAAAAII/9H0JrVzsqb8RYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x36 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 100% train data\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = [TSStandardize(by_sample=True)]\n",
    "check_data(X, y, splits)\n",
    "dls100 = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "udls100 = get_ts_dls(X, splits=splits, tfms=tfms, batch_tfms=batch_tfms) # used in pretraining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also need a labeled dataloaders with 10% of the training and 100% of validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X      - shape: [4925 samples x 6 features x 36 timesteps]  type: memmap  dtype:float32  isnan: 0\n",
      "y      - shape: (4925,)  type: memmap  dtype:<U2  n_classes: 14 (351 samples per class) ['15', '16', '42', '52', '53', '6', '62', '64', '65', '67', '88', '90', '92', '95']  isnan: False\n",
      "splits - n_splits: 2 shape: [245, 2466]  overlap: [False]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x36 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10% train data\n",
    "train_split010 = get_splits(y[splits[0]], valid_size=.1, show_plot=False)[1]\n",
    "splits010 = (train_split010, splits[1])\n",
    "check_data(X, y, splits010)\n",
    "dls010 = get_ts_dls(X, y, splits=splits010, tfms=tfms, batch_tfms=batch_tfms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised 👀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we'll train a model in a supervised way to set a baseline. We'll train using 10% and 100% of the training set. We'll run 10 tests.\n",
    "\n",
    "We'll train all models with the same settings (50 epochs with 10% of labels, 20 and 50 epochs with 100% of labels and lr=1e-2) to see the impact of pretraining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 accuracy: 0.565 +/- 0.010\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.521446</td>\n",
       "      <td>2.603504</td>\n",
       "      <td>0.018914</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.252840</td>\n",
       "      <td>2.549198</td>\n",
       "      <td>0.183799</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.035993</td>\n",
       "      <td>2.449395</td>\n",
       "      <td>0.298109</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.846420</td>\n",
       "      <td>2.289011</td>\n",
       "      <td>0.338816</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.693431</td>\n",
       "      <td>2.042925</td>\n",
       "      <td>0.382401</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.547713</td>\n",
       "      <td>1.903890</td>\n",
       "      <td>0.388158</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.442364</td>\n",
       "      <td>2.065079</td>\n",
       "      <td>0.303454</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.362495</td>\n",
       "      <td>3.627480</td>\n",
       "      <td>0.201891</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.278991</td>\n",
       "      <td>6.207148</td>\n",
       "      <td>0.214638</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.199572</td>\n",
       "      <td>4.038610</td>\n",
       "      <td>0.328947</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.151962</td>\n",
       "      <td>8.653461</td>\n",
       "      <td>0.273438</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.110085</td>\n",
       "      <td>14.308350</td>\n",
       "      <td>0.370477</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.063458</td>\n",
       "      <td>8.315976</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.025232</td>\n",
       "      <td>6.702715</td>\n",
       "      <td>0.334704</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.977443</td>\n",
       "      <td>3.799024</td>\n",
       "      <td>0.413651</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.928115</td>\n",
       "      <td>4.110534</td>\n",
       "      <td>0.395970</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.876544</td>\n",
       "      <td>3.366133</td>\n",
       "      <td>0.486020</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.825293</td>\n",
       "      <td>2.936010</td>\n",
       "      <td>0.412829</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.775785</td>\n",
       "      <td>2.646344</td>\n",
       "      <td>0.465872</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.726957</td>\n",
       "      <td>2.334246</td>\n",
       "      <td>0.513980</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.681581</td>\n",
       "      <td>2.412533</td>\n",
       "      <td>0.491365</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.639972</td>\n",
       "      <td>2.709539</td>\n",
       "      <td>0.532895</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.602204</td>\n",
       "      <td>2.818977</td>\n",
       "      <td>0.467928</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.565576</td>\n",
       "      <td>2.634877</td>\n",
       "      <td>0.414062</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.530369</td>\n",
       "      <td>2.448482</td>\n",
       "      <td>0.443257</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.496120</td>\n",
       "      <td>2.452373</td>\n",
       "      <td>0.468750</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.463077</td>\n",
       "      <td>2.582138</td>\n",
       "      <td>0.451069</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.434846</td>\n",
       "      <td>2.454903</td>\n",
       "      <td>0.469572</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.406917</td>\n",
       "      <td>2.214773</td>\n",
       "      <td>0.528783</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.381470</td>\n",
       "      <td>2.108379</td>\n",
       "      <td>0.559622</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.357005</td>\n",
       "      <td>2.113634</td>\n",
       "      <td>0.573602</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.333850</td>\n",
       "      <td>2.270672</td>\n",
       "      <td>0.563734</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.312089</td>\n",
       "      <td>2.349197</td>\n",
       "      <td>0.560444</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.291865</td>\n",
       "      <td>2.304734</td>\n",
       "      <td>0.565378</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.273127</td>\n",
       "      <td>2.256665</td>\n",
       "      <td>0.567845</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.255664</td>\n",
       "      <td>2.224910</td>\n",
       "      <td>0.570724</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.239321</td>\n",
       "      <td>2.197691</td>\n",
       "      <td>0.571135</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.224152</td>\n",
       "      <td>2.172345</td>\n",
       "      <td>0.573191</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.210058</td>\n",
       "      <td>2.166167</td>\n",
       "      <td>0.570312</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.196809</td>\n",
       "      <td>2.154570</td>\n",
       "      <td>0.567023</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.184430</td>\n",
       "      <td>2.161891</td>\n",
       "      <td>0.567845</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.172940</td>\n",
       "      <td>2.168623</td>\n",
       "      <td>0.568668</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.162179</td>\n",
       "      <td>2.167025</td>\n",
       "      <td>0.568257</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.152198</td>\n",
       "      <td>2.153521</td>\n",
       "      <td>0.567434</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.142931</td>\n",
       "      <td>2.153600</td>\n",
       "      <td>0.567023</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.134189</td>\n",
       "      <td>2.167169</td>\n",
       "      <td>0.567845</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.125996</td>\n",
       "      <td>2.159749</td>\n",
       "      <td>0.567023</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.118371</td>\n",
       "      <td>2.164366</td>\n",
       "      <td>0.568257</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.111274</td>\n",
       "      <td>2.160060</td>\n",
       "      <td>0.569079</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.104632</td>\n",
       "      <td>2.162913</td>\n",
       "      <td>0.569079</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.565 +/- 0.010 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# supervised 10%\n",
    "n_epochs = 50\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls010, InceptionTimePlus, metrics=accuracy)\n",
    "    learn.fit_one_cycle(n_epochs, 1e-2)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.713 +/- 0.009\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.667220</td>\n",
       "      <td>1.327950</td>\n",
       "      <td>0.606497</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.331364</td>\n",
       "      <td>1.117478</td>\n",
       "      <td>0.633224</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.164190</td>\n",
       "      <td>1.103409</td>\n",
       "      <td>0.637747</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.072654</td>\n",
       "      <td>1.368326</td>\n",
       "      <td>0.547697</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.014387</td>\n",
       "      <td>1.119432</td>\n",
       "      <td>0.637747</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.948147</td>\n",
       "      <td>1.180676</td>\n",
       "      <td>0.621299</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.943178</td>\n",
       "      <td>1.278045</td>\n",
       "      <td>0.580181</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.916846</td>\n",
       "      <td>1.110911</td>\n",
       "      <td>0.634046</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.900196</td>\n",
       "      <td>1.214414</td>\n",
       "      <td>0.654605</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.876018</td>\n",
       "      <td>1.001013</td>\n",
       "      <td>0.649671</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.847380</td>\n",
       "      <td>1.014243</td>\n",
       "      <td>0.668174</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.801280</td>\n",
       "      <td>1.031421</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.740619</td>\n",
       "      <td>1.073294</td>\n",
       "      <td>0.654605</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.692921</td>\n",
       "      <td>1.006668</td>\n",
       "      <td>0.670230</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.626234</td>\n",
       "      <td>1.503119</td>\n",
       "      <td>0.548931</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.591007</td>\n",
       "      <td>1.316109</td>\n",
       "      <td>0.617599</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.531313</td>\n",
       "      <td>1.120147</td>\n",
       "      <td>0.678865</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.501271</td>\n",
       "      <td>1.331397</td>\n",
       "      <td>0.658717</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.444844</td>\n",
       "      <td>1.299146</td>\n",
       "      <td>0.655428</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.386926</td>\n",
       "      <td>1.383517</td>\n",
       "      <td>0.616776</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.340941</td>\n",
       "      <td>1.826334</td>\n",
       "      <td>0.659128</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.327079</td>\n",
       "      <td>1.384788</td>\n",
       "      <td>0.694490</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.268344</td>\n",
       "      <td>1.554343</td>\n",
       "      <td>0.664885</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.223414</td>\n",
       "      <td>1.521488</td>\n",
       "      <td>0.641036</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.176642</td>\n",
       "      <td>1.398141</td>\n",
       "      <td>0.676809</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.141317</td>\n",
       "      <td>2.016772</td>\n",
       "      <td>0.668997</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.113236</td>\n",
       "      <td>1.686299</td>\n",
       "      <td>0.648438</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.125124</td>\n",
       "      <td>1.903873</td>\n",
       "      <td>0.663651</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.125599</td>\n",
       "      <td>1.590350</td>\n",
       "      <td>0.663240</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.096613</td>\n",
       "      <td>1.726169</td>\n",
       "      <td>0.708059</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.066549</td>\n",
       "      <td>1.711092</td>\n",
       "      <td>0.679688</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.044989</td>\n",
       "      <td>1.723265</td>\n",
       "      <td>0.682155</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.033672</td>\n",
       "      <td>1.754556</td>\n",
       "      <td>0.680921</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.024706</td>\n",
       "      <td>1.848146</td>\n",
       "      <td>0.685855</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.023254</td>\n",
       "      <td>2.015317</td>\n",
       "      <td>0.683799</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.020973</td>\n",
       "      <td>2.005220</td>\n",
       "      <td>0.677220</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.022136</td>\n",
       "      <td>1.979869</td>\n",
       "      <td>0.695312</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.019864</td>\n",
       "      <td>1.939935</td>\n",
       "      <td>0.696957</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.014194</td>\n",
       "      <td>1.844270</td>\n",
       "      <td>0.692434</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.009608</td>\n",
       "      <td>1.901104</td>\n",
       "      <td>0.696135</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.006581</td>\n",
       "      <td>1.914474</td>\n",
       "      <td>0.705592</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.004520</td>\n",
       "      <td>1.938651</td>\n",
       "      <td>0.700247</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>1.976140</td>\n",
       "      <td>0.696957</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.002488</td>\n",
       "      <td>1.964839</td>\n",
       "      <td>0.699836</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.001920</td>\n",
       "      <td>1.968988</td>\n",
       "      <td>0.701069</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.002478</td>\n",
       "      <td>1.976972</td>\n",
       "      <td>0.700247</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.001774</td>\n",
       "      <td>1.954741</td>\n",
       "      <td>0.699836</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>1.967127</td>\n",
       "      <td>0.701069</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.001223</td>\n",
       "      <td>1.987314</td>\n",
       "      <td>0.698602</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.001146</td>\n",
       "      <td>1.976043</td>\n",
       "      <td>0.699013</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.711 +/- 0.010 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# supervised 100%\n",
    "n_epochs = 50\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls100, InceptionTimePlus, metrics=accuracy)\n",
    "    learn.fit_one_cycle(n_epochs, 1e-2)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've also trained the model with all labels for 20 as it seems to be overfitting with 50 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.716 +/- 0.006\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.647807</td>\n",
       "      <td>1.336298</td>\n",
       "      <td>0.601974</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.343674</td>\n",
       "      <td>1.304147</td>\n",
       "      <td>0.615132</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.206320</td>\n",
       "      <td>1.346143</td>\n",
       "      <td>0.590049</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.134852</td>\n",
       "      <td>1.452480</td>\n",
       "      <td>0.558799</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.049085</td>\n",
       "      <td>1.162987</td>\n",
       "      <td>0.658717</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.984902</td>\n",
       "      <td>1.031802</td>\n",
       "      <td>0.675164</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.922748</td>\n",
       "      <td>1.037294</td>\n",
       "      <td>0.649671</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.857191</td>\n",
       "      <td>1.063216</td>\n",
       "      <td>0.678454</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.785906</td>\n",
       "      <td>1.139487</td>\n",
       "      <td>0.647615</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.729185</td>\n",
       "      <td>1.019662</td>\n",
       "      <td>0.689967</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.658080</td>\n",
       "      <td>0.938686</td>\n",
       "      <td>0.707648</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.570342</td>\n",
       "      <td>1.173928</td>\n",
       "      <td>0.637336</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.496892</td>\n",
       "      <td>1.027795</td>\n",
       "      <td>0.705181</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.405444</td>\n",
       "      <td>1.073043</td>\n",
       "      <td>0.678454</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.325335</td>\n",
       "      <td>1.067052</td>\n",
       "      <td>0.702714</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.245655</td>\n",
       "      <td>1.180020</td>\n",
       "      <td>0.716283</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.184259</td>\n",
       "      <td>1.130675</td>\n",
       "      <td>0.701069</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.137108</td>\n",
       "      <td>1.141029</td>\n",
       "      <td>0.712582</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.101256</td>\n",
       "      <td>1.168026</td>\n",
       "      <td>0.718750</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.082959</td>\n",
       "      <td>1.173548</td>\n",
       "      <td>0.715872</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.716 +/- 0.005 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# supervised 100%\n",
    "n_epochs = 20\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls100, InceptionTimePlus, metrics=accuracy)\n",
    "    learn.fit_one_cycle(n_epochs, 1e-2)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a great result. Just for reference, in a recent review of multivariate time series models (Ruiz, A. P., Flynn, M., Large, J., Middlehurst, M., & Bagnall, A. (2020). The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, 1-49.), the best performing classifier on this dataset is MUSE with an accuracy of **63.62**. \n",
    "\n",
    "Let's see if we can improve our baseline pretraining InceptionTime using `MVP`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pretrain model  🏋️‍♂️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll train a model without any labels on the entire training set. To do that we need to use the `MVP` callback. You can get more details on this callback visiting [`tsai` documentation](https://timeseriesai.github.io/tsai/callback.MVP)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.824511</td>\n",
       "      <td>0.695164</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.742146</td>\n",
       "      <td>0.624149</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.682734</td>\n",
       "      <td>0.619673</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.650123</td>\n",
       "      <td>0.591099</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.627980</td>\n",
       "      <td>0.590760</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.608256</td>\n",
       "      <td>0.564849</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.596241</td>\n",
       "      <td>0.580909</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.588619</td>\n",
       "      <td>0.572586</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.575416</td>\n",
       "      <td>0.584031</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.553148</td>\n",
       "      <td>0.522473</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.548571</td>\n",
       "      <td>0.549207</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.548863</td>\n",
       "      <td>0.547475</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.546617</td>\n",
       "      <td>0.527021</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.535787</td>\n",
       "      <td>0.549150</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.544342</td>\n",
       "      <td>0.518173</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.531605</td>\n",
       "      <td>0.520816</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.521335</td>\n",
       "      <td>0.527413</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.530482</td>\n",
       "      <td>0.538147</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.530002</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.520098</td>\n",
       "      <td>0.517891</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.522009</td>\n",
       "      <td>0.515128</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.509363</td>\n",
       "      <td>0.530004</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.507416</td>\n",
       "      <td>0.518783</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.503394</td>\n",
       "      <td>0.501925</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.492000</td>\n",
       "      <td>0.505918</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.498571</td>\n",
       "      <td>0.508154</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.508513</td>\n",
       "      <td>0.498059</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.510197</td>\n",
       "      <td>0.530474</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.497853</td>\n",
       "      <td>0.498322</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.497102</td>\n",
       "      <td>0.507167</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.488492</td>\n",
       "      <td>0.474423</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.491783</td>\n",
       "      <td>0.488287</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.500977</td>\n",
       "      <td>0.512166</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.489673</td>\n",
       "      <td>0.489390</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.488730</td>\n",
       "      <td>0.502970</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.479257</td>\n",
       "      <td>0.491193</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.477008</td>\n",
       "      <td>0.495478</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.481075</td>\n",
       "      <td>0.497181</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.476612</td>\n",
       "      <td>0.466445</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.477732</td>\n",
       "      <td>0.479337</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.475114</td>\n",
       "      <td>0.485341</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.473457</td>\n",
       "      <td>0.490123</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.480115</td>\n",
       "      <td>0.469441</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.474718</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.473582</td>\n",
       "      <td>0.458616</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.476582</td>\n",
       "      <td>0.464176</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.466654</td>\n",
       "      <td>0.471503</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.477277</td>\n",
       "      <td>0.484061</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.467621</td>\n",
       "      <td>0.469101</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.473150</td>\n",
       "      <td>0.455566</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.464150</td>\n",
       "      <td>0.460312</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.461221</td>\n",
       "      <td>0.465207</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.452839</td>\n",
       "      <td>0.459377</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.458763</td>\n",
       "      <td>0.483846</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.464921</td>\n",
       "      <td>0.452392</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.457033</td>\n",
       "      <td>0.452094</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.452562</td>\n",
       "      <td>0.471176</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.449554</td>\n",
       "      <td>0.460122</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.445565</td>\n",
       "      <td>0.464390</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.445551</td>\n",
       "      <td>0.443181</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.451970</td>\n",
       "      <td>0.433638</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.437998</td>\n",
       "      <td>0.453156</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.429256</td>\n",
       "      <td>0.449684</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.443158</td>\n",
       "      <td>0.430788</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.437519</td>\n",
       "      <td>0.464656</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.442310</td>\n",
       "      <td>0.461007</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.453108</td>\n",
       "      <td>0.439876</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.437026</td>\n",
       "      <td>0.421071</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.432636</td>\n",
       "      <td>0.470044</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.426164</td>\n",
       "      <td>0.444493</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.436965</td>\n",
       "      <td>0.445740</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.428231</td>\n",
       "      <td>0.456754</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.427984</td>\n",
       "      <td>0.447773</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.429965</td>\n",
       "      <td>0.440068</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.428938</td>\n",
       "      <td>0.439552</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.429434</td>\n",
       "      <td>0.453853</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.432046</td>\n",
       "      <td>0.440990</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.429124</td>\n",
       "      <td>0.426548</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.426853</td>\n",
       "      <td>0.446997</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.438779</td>\n",
       "      <td>0.442617</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.432320</td>\n",
       "      <td>0.439846</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.429860</td>\n",
       "      <td>0.434093</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.424093</td>\n",
       "      <td>0.445661</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.422853</td>\n",
       "      <td>0.439658</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.417969</td>\n",
       "      <td>0.457689</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.428347</td>\n",
       "      <td>0.436601</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.419401</td>\n",
       "      <td>0.441211</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.423933</td>\n",
       "      <td>0.428135</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.418072</td>\n",
       "      <td>0.442335</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.417775</td>\n",
       "      <td>0.426060</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.424482</td>\n",
       "      <td>0.420594</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.421221</td>\n",
       "      <td>0.418174</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.410412</td>\n",
       "      <td>0.425998</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.412839</td>\n",
       "      <td>0.441237</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.428310</td>\n",
       "      <td>0.441229</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.419316</td>\n",
       "      <td>0.435748</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.417024</td>\n",
       "      <td>0.420680</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.413396</td>\n",
       "      <td>0.419322</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.413778</td>\n",
       "      <td>0.440554</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.416570</td>\n",
       "      <td>0.447854</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.410792</td>\n",
       "      <td>0.448979</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.408801</td>\n",
       "      <td>0.438739</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.405882</td>\n",
       "      <td>0.445163</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.411996</td>\n",
       "      <td>0.440078</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.400889</td>\n",
       "      <td>0.423820</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.399643</td>\n",
       "      <td>0.437745</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>0.398071</td>\n",
       "      <td>0.428906</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.399078</td>\n",
       "      <td>0.429259</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>0.403977</td>\n",
       "      <td>0.429331</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>0.406988</td>\n",
       "      <td>0.419934</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>0.396265</td>\n",
       "      <td>0.431182</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>0.400656</td>\n",
       "      <td>0.426436</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>0.393257</td>\n",
       "      <td>0.434342</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>0.404205</td>\n",
       "      <td>0.421863</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>0.396941</td>\n",
       "      <td>0.445141</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>0.387929</td>\n",
       "      <td>0.442237</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>0.390414</td>\n",
       "      <td>0.424312</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>0.395546</td>\n",
       "      <td>0.419235</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>0.389951</td>\n",
       "      <td>0.414988</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>0.385452</td>\n",
       "      <td>0.414802</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.388758</td>\n",
       "      <td>0.405732</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>0.390608</td>\n",
       "      <td>0.423665</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>0.387738</td>\n",
       "      <td>0.413649</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>0.392759</td>\n",
       "      <td>0.420396</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>0.390528</td>\n",
       "      <td>0.428076</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>0.387268</td>\n",
       "      <td>0.430213</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>0.385750</td>\n",
       "      <td>0.401695</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>0.380813</td>\n",
       "      <td>0.427017</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>0.386075</td>\n",
       "      <td>0.410972</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>0.387639</td>\n",
       "      <td>0.426368</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>0.386465</td>\n",
       "      <td>0.436176</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>0.382195</td>\n",
       "      <td>0.419468</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>0.374583</td>\n",
       "      <td>0.417876</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>0.373527</td>\n",
       "      <td>0.419694</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>0.383271</td>\n",
       "      <td>0.439733</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>0.375390</td>\n",
       "      <td>0.427540</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>0.375719</td>\n",
       "      <td>0.432903</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>0.369254</td>\n",
       "      <td>0.411961</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>0.371220</td>\n",
       "      <td>0.428568</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>0.373135</td>\n",
       "      <td>0.438605</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>0.378706</td>\n",
       "      <td>0.420879</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>0.374695</td>\n",
       "      <td>0.430965</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>0.376679</td>\n",
       "      <td>0.428427</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>0.367513</td>\n",
       "      <td>0.426240</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>0.366111</td>\n",
       "      <td>0.421254</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0.361267</td>\n",
       "      <td>0.422222</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>0.368960</td>\n",
       "      <td>0.411377</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.364625</td>\n",
       "      <td>0.418780</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>0.375936</td>\n",
       "      <td>0.415644</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.356617</td>\n",
       "      <td>0.432023</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.357665</td>\n",
       "      <td>0.406491</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.353387</td>\n",
       "      <td>0.412494</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>0.366133</td>\n",
       "      <td>0.416451</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>0.365733</td>\n",
       "      <td>0.414979</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>0.359868</td>\n",
       "      <td>0.423053</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>0.349877</td>\n",
       "      <td>0.417995</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>0.355785</td>\n",
       "      <td>0.414486</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>0.352739</td>\n",
       "      <td>0.414499</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>0.345617</td>\n",
       "      <td>0.409995</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>0.358122</td>\n",
       "      <td>0.416199</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.357337</td>\n",
       "      <td>0.420905</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>0.351488</td>\n",
       "      <td>0.411797</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>0.348311</td>\n",
       "      <td>0.399008</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>0.425630</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>0.353291</td>\n",
       "      <td>0.424499</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>0.352910</td>\n",
       "      <td>0.426359</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>0.358350</td>\n",
       "      <td>0.424517</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>0.351027</td>\n",
       "      <td>0.423423</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>0.346842</td>\n",
       "      <td>0.426368</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>0.348844</td>\n",
       "      <td>0.426346</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>0.345428</td>\n",
       "      <td>0.403963</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>0.336016</td>\n",
       "      <td>0.408110</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>0.339641</td>\n",
       "      <td>0.427894</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>0.341885</td>\n",
       "      <td>0.424336</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0.345443</td>\n",
       "      <td>0.416860</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>0.346384</td>\n",
       "      <td>0.415427</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>0.343906</td>\n",
       "      <td>0.434155</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>0.343823</td>\n",
       "      <td>0.423307</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>0.347063</td>\n",
       "      <td>0.419656</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>0.345853</td>\n",
       "      <td>0.423150</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>0.340230</td>\n",
       "      <td>0.411173</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>0.337354</td>\n",
       "      <td>0.429679</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>0.336735</td>\n",
       "      <td>0.415280</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.334650</td>\n",
       "      <td>0.413658</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>0.340324</td>\n",
       "      <td>0.416536</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>0.341128</td>\n",
       "      <td>0.407453</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>0.341340</td>\n",
       "      <td>0.400430</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>0.345199</td>\n",
       "      <td>0.422862</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>0.339615</td>\n",
       "      <td>0.410507</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>0.336302</td>\n",
       "      <td>0.437459</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>0.341796</td>\n",
       "      <td>0.406272</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>0.340041</td>\n",
       "      <td>0.419963</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>0.338672</td>\n",
       "      <td>0.403852</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.336762</td>\n",
       "      <td>0.416917</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>0.332332</td>\n",
       "      <td>0.414000</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>0.344714</td>\n",
       "      <td>0.405989</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>0.345953</td>\n",
       "      <td>0.432160</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>0.341632</td>\n",
       "      <td>0.418454</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>0.339039</td>\n",
       "      <td>0.398258</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>0.344143</td>\n",
       "      <td>0.425503</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pre-trained model weights_path='data/TSBERT/LSST_200.pth'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Unlabeled 100%\n",
    "learn = ts_learner(udls100, InceptionTimePlus, cbs=[ShowGraph(), MVP(target_dir='./data/MVP', fname=f'{dsid}_200')])\n",
    "learn.fit_one_cycle(200, 1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SZQ8rBADsJwJCAEBDWWt1+Y0ZzV5PP/LY5NyM3F6fQrH4PlQGAGgFc+miKjWroVjjpxff1Rn0aqXNOwh/Pp6U3+PUMwPRbZ97ZiSmSs3q0tTqHlQGAGgGAkIAQEPlMyXl0uuav7WqarW26XG1WlWpuVnF+gZkHDyOAAB1U6m8HMZoYA8DwkSHVyvZddVq7bnExfxqUePLOZ0dicnj2v4zOB706FBXUJem0ypv8awHALQOPpEBABoqvVBfWL5Sqio5k9v0uMzSkqrlkmL9A/tVGgCgBUyn8uqNeOV1OffsGp1Bjyo1q9VCec+ucZC9M74in/vx1h7czNmRmAqlqj6ezTSwMgBAsxAQAgAaKr2QVzDqVSDs0cL46qYbkCRnp+RwuhTt6dvnCgEAB5W1VsvZkro7fHt6nXs7Gefabx3CxUxRt5ZyOjMc3VUIOxD1qzfi08XJVNt2YgLAk4SAEADQMKViRbn0umK9AfWMRZTPlLSWLD50nLVWqdkZRXp65XQ1fodKAEBrypWqKlVqigU9e3qdzpBHxkhLa+23DuHb40l53Q6dHtr+2oP3M8bo7EhM6XxZt5azDaoOANAsBIQAgIZZXSxIkiLdAcUHgnJ5nFoYf3jqUS6d0no+p3j/4H6XCAA4wFK5emAXD+xtQOh2OhTxu9uug3Bxraibi1mdGY7J5979FO4jXSFF/G5dmEg1oDoAQDMREAIAGia9mJfH71Ig7JHT6VDXUIfSC3mtFyoPHJeanZZkFO9j/UEAwKeSdwLCWNC959fqDLXfTsbv3Kp3Dz63y+7BuxwOozMjMc2mi5pNFxoyJgCgOQgIAQANUavWtLpUULQ7IGOMJKl7tEPWSosTD3YRJmen1ZFIyO3b2zWmAACtJZkvyeNyKOTd++UnEiGPUvlS2+zCu7S2rhuLWT03FG1I9+BdT/WF5XM7dZ4uQgBoaQSEAICGyKwUVavUFO0J3HvNG3Ar1hPQ0sSaqnc+gBVzWeXSKcX7mF4MAHhQKldSLOC594OmvZQIeWXtp9Oan3Q/H0/K43LozHCsoeN6XA6dHozo1lK2bf4sAeBJREAIAGiI9EJeDqdDHYkHuwJ7xsKqlKpKzuQkSanZGUlSfICAEADwoGSupPg+TC+WpM47G6Est8E045Xsuq4vrjW8e/Cu00NROY3RxUm6CAGgVREQAgB2zVqr1cWCwl0+OZ0PPlo6On3yd3i0cDsja62Ss9Pyd4Tl7wg3qVoAwEFUqtS0VqwoHvTuy/ViAY9cDqPl7JO/UcnPx5NyOxvfPXhX0OvSyb6wPp7NKF+qPPoEAMCBQ0AIANi1wlpZ6/myot2Bh94zxqhnLKz86rrSi2vKLC0q3j/UhCoBAAdZKn9nB+N96iB0OIxiQc8Tv5NxMlfStYU1nR6Myu9pfPfgXWdGYqrUrC5Nre7ZNQAAe2fvV/8FADzx0ot5SdowIJSkzoGQpq4kNf7eDVlbU6yf3YsBAA9K5kqSteq5/W+kGympo08KD0jhvvrvXY3vLEyEvJpO5Rs+7kHy8/EVuRxGZ0Yas3PxZuJBjw51BXVpOq1zozG5nfSiAEAraVhAaIxxSjovacZa+9VGjQsAOPjSC3kFIl55/Bs/Vpwuh7qGOvTRjy4q3udVR2dinysEABx0qVxJncUJBbOTUmxEyq9Iy9frbxojBTrvhIZ9Uke/FOqWHLvriEuEPLoyl1GxXN2TtfmaLZUr6er8ms4MxxTw7H1vyNmRmP7q/LQ+ns3o9NDeBpIAgMZq5FPiP5N0RRKLSgFAGymXqsqm1tV/dOsPAomhoAqZJZmBw/uyOyUAoLUkc0Udy12Qoychnf73JIdDKhektTkpMytl5qTkTWn+w/oJDpcU7pdOflXyRXZ0zc5QvStxObuuwdjGXfCt7Oe3k3I5jM6O7M3ag581EPWrN+LTxcmUnhmIyOHgeQ8AraIhAaExZlDSVyT9N5L+oBFjAgBaw+piXrJWsZ6tP1it55Py+KVyOaxatSYHU48AAPcxcx8oqox06Lfq4aAkuf1S/FD9S5KslYqrn4aGMxelibek47+2o2smQvWdjFeypScyIHxuKKqBqF9B7/6sLGVMPYz8zgdzurWc1ZHujn25LgBg9xr16ex/lPQNSbXNDjDGfN0Yc94Yc35paalBlwUANFt6oSC316VAxLPlcanZGXV0BuXyxJScy+1TdZvjuQQAB0etvK7wwttyxIakxNHNDzRG8kel7pPSkS9Jvc/UOwrXszu6bsjrktftODA7GTf62dQT9unpgZ11V+7Uka6QIn63Lkyk9vW6AIDd2XVAaIz5qqRFa+2FrY6z1n7TWnvOWnuuq6trt5cFABwAtZrV6lJe0R7/ltOGrbVKzk6r99CwAmG/FsYzstbuY6Ub1sRzCQAOiNytt+Sq5GXHfrEeAj6uoRclW5VmtvwosiljjBJBr1aypR2d32hPwrPJ4TA6MxLTbLqo2XSh2eUAAB5TIzoIPy/p7xtjbkv6vyT9kjHmzxowLgDggMsmi6qWa4o+YnpxLpVUqZBXfGBQPWNh5dLryqUPRrcGAKDJSjmVx99S0j+qjp7R7Z0biEuJY/WAsLKzkC/R4dFybr3pP7h6kjzVF5bP7dR5uggBoGXsOiC01v6htXbQWjsq6Xck/Ttr7X+w68oAAAdeeiEvh9MonPBveVxydlqSUayvX4nBkJxuhxbGM/tTJADgYJv4mYrr65qMvqh4cOvlKjY09JJUWZfmLu3o8p1Br9bLNa2tV3Z0Ph7mcTl0ejCiW0tZpXIHozsTALA1VogHAOxYejGvjk6/nK6tHyfJ2RmFE11ye31yuhxKDHUoOZdTqciHMQBoa/mkNPueFoLH5Qh2yud2bn+MyIAUHZamfy7Vqhse8t7ie/rWjW9t2CXYed9GJWic00NROY3Re1N0EQJAK2hoQGit/ZG19quNHBMAcDAVsiUVs2VFu7eeXlzMZpVfTSk+MHjvtZ6RsGzNamliba/LBAAcZOM/kYzRePB5xXbSPXjX0EtSMSMtXtnw7eup65rNzmp8dfyh9xIhryQdmI1KnhRBr0tHezp0ZW5Npcqme1kCAA4IOggBADuSXqgvPB7teZzpxVKs/9OA0BdyK9Id0OLEmmo11nwCgLaUmasHeoMvarHkVjywi4Cw87AUTEhTb0uf6RIsVUtaKaxIki4sXnioi9DndqrD59IKAWHDPT0QVqlS0/VFfiAIAAcdASEAYEfSC3kFwh55A+4tj0vOTisQjsof6njg9Z6xsMrrFSVnc3tZJgDgILJWuvVDye1XofcFFUrV3XUQGlPvIswuSclbD7y1kFuQldXh6GEt5Zc0nZ1+6PTOkEfLTDFuuIGoX7GAW5dnWHcYAA46AkIAwLZVSlVlU0VFHrF7cbm0rrXlJcX6Bx56L9Llly/k1uJtPjQAQNtJ3pJSE9Loq0qWjCTtbIOS+/Wckrwd0tQ7D7w8l5uTkdFrg68p6A7q4sLFh05NhLxK5kqq0tXeUMYYPT0Q0Uy6oCSblQDAgUZACADYttWlgmzNPnL9wdTsjKytPbD+4F3GGHWPhpVNFZVNMa0LANpGrVbvHvRHpf7n7+1yu6spxpLkcEqDL9SDx8zsvZfncnPq9HfK7/LrdNdpzWRnNJ+bf+DUzqBX1ZpVOk+I1Wgn+8JyGKOPZlabXQoAYAsEhACAbUsv5uXyOBWKerc8LjU3I4/Pr1Csc8P3uwY7dPhMtwKRXX4oBAC0jsXL9anAY1+UHE4lcyW5HEYdPtfux+5/TnJ573URVmtVLeYX1RfskySd6jwlr9P7UBdhouPOTsZ0uTVc0OvSoa6grsxl6NAEgAOMgBAAsC22ZrW6WFC0OyDjMJseV6tWlZ6fU6x/UMZsfJzT7VDnQEiOLcYBADxBqpX6zsUdvVL3SUlSKl9SLOhpzLPA5ZX6n5eWrkn5pFaKKyrXyvcCQrfTrWe7ntXtzG0tF5bvnRYPeOQwRstrdLTvhacHIsqXqhpfzja7FADAJggIAQDbspYqqlKqKvqI9QdXF+dVrZQV32D9QQBAm5q5IBUz0qFfqG8sIimZK+1+/cH7DZ6TjEOafldzuTlJUm+w997bzySekdvh1nuL7917zeV0KBpwa5kOwj0xEg+ow+fSR2xWAgAHFgEhAGBbVhcKMg6jSJd/y+OSs9NyutyKdPdueRwAoE2UC9Lkz6T4ISk+JkmqVGtaLZQV2+36g/fzdkg9T0tzH2hu9bY6PB0KeUL33va5fDrVeUo3Uje0uv7puniJkFcrWToI94LDYfRUX1i3V3LKFMvNLgcAsAECQgDAtqQX8+ro9Mnp3vwRYq1VcnZG0d4+OZzOfawOAHBgTb4lVdbr3YN3pPJlWduAHYw/a+gl2VpF87MX7k0vvt/p7tMyxuj9xffvvdYZ8mi1UFapUmtsLZAkneqPyFrp49kNughXbkrn/7U0faG+iQ0AYN8REAIAHlsxV1ZhrfTI3YuzyRWViwXF+x/evRgA0IaKq/Xwp+eU1NFz7+XUnV2DY0F3Y68X7FQmOqh8elx9vsTDb7uDOhk/qSvJK8qVc5KkRMgja+tTntF4kYBbw/GALs9mZO2dzUqslSbekj78K6mYlq5/Tzr/J1LqdlNrBYB2REAIAHhs6cW8JD1y/cHk7LSMcSja178fZQEADrrZ9yVblUa/8MDLd8O4hk4xvmMuPixVK+rNJTd8/7nu52St1aXFS5LqU4wlaZlpxnvm6YGIMoWyppIFqVKSPv430q0fSV0npM/9J9LTvyVVy9L7fyFd/lupkG52yQDQNlzNLgAA0DrSC3n5Qh75tuj0sNYqOTOlcFe33B7vPlYHADiwlj+RIkOSP/rAy6lcSWG/W25n4/sW5kxNHn9c8fkr0ugvSI4HrxHxRnQkdkSXVy7rTM8ZRfxeuZ2GgHAPHe4Kyud26urtKQ3X3pRyS/Up58Ofq29a03Wsvj7l1M/r61Wu3JCGX5aGXpKcDe4yBQA8gA5CAMBjqZZrWlspKtqz9eYk6YU5FdYySgyP7k9hAICDLbdc/+o68dBbyXxJ8UZPL75jPj+vvr4zMusZaenKhsc83/28yrWyPlz+UMYYxYNerWSZYrxXXE6Hnu9IK/Dh/6FyLi0989vSyMv3drSWVA8CRz8vvfh1qfOINP6G9PP/VVq8Wp+SDADYEwSEAIDHsrpUkK3ZR04vnrn6sdyhSP4AACAASURBVDz+gLpGRvenMADAwbZ0rf5r4ugDL1trlcqV9mR6caFSUKqYUl/Pc1KgU5p8e8NwKeFPaDQ8qg+WPlC5WlYi5NFKjg7CPWGtNPWunln5O5UcPn3c/zWp8/Dmx/si0qmvSc/9+5LLU59yfOkvpOzS/tUMAG2EgBAA8Ejl9aqmriTl9rnUEfNtetzayrIySwvqP3ZCDge7FwMAJC1dlSIDki/8wMtr6xWVq7bxOxhLms/NS5L6Qv3S8EtSdlFKjW947JmeM1qvruvyymV1hrzKrVeVL1UaXlNbq1akq9+Rbnxfwb7jWjr6O/pgxfHpZiVbiY1IZ/+xdPRXpOyCdP5f1bsKAQANRUAIANhSrVrT9fMLKq9XdPRct4zDbHrszNXLcnm86jl0ZB8rBAAcWIVUPZxLHH/ordReblCSm5PDONQV6JK6T0neUH1duw30Bns1EBrQpaVLigfqP9ximnEDFTPS+38mzX8ojb4qPf1bemqoS8vZkuYzxccbw+GQBs9KL/5HUtdx6fabUmpib+sGgDZDQAgA2JS1VuMfLCubLGrsuS6FtugezGdWlZydVt+RY3K6WEgcAKBPpxd3HXvorbs7GO9VB2F3oFsuh0tyuqS+56TUbWk9u+HxZ7rPKFfOKVm5LYmdjBtmdVq68L/V16B8+reksS9IxuhYb0gel0MfzWS2N54nIJ34Sn368fXvSbXqnpQNAO2IgBAAsKnZ62mtTGc1eCKmzv7QlsfOXL0sh9Ol3iMPfwgEALSppWtSR4/kjz30VipfktftUMDT2CUpyrWyFvOL6g32fvpi1/H6Gngr1zc8Z7BjUF2BLl1JfaDjvUF1+PhB165V1qUP/1pyeqSzv/dASOx1OXW0O6RPFtZUqtS2N67TLR39cj10nD7f2JoBoI0REAIANrQyk9XMtZQSgx3qOxLd8thiLqvlyQn1HDoit3fzLkMAQBspZqTM7Ia7F0tSMldWPOCRMZsvXbETS/kl1WxNfcG+T18Mdkn+qLS8cUBojNHZ7rPKlDI6OpjXke6tfyiGxzBzQSoXpJN/TwomHnr76YGISpWaPllY2/7YnUfqX7ffqP89AwDsGgEhAOAha8mixi8tqaPTp9FnOx/54W3uk6uSkfqPbfwhEADQhpY/qf+6wfqDUn0NwtgeTC+ey81J0oMdhMbUd1FO3a53tm1gLDKmmC+miwsXH2/zDGyuXJSm3qmHeJGBDQ/pi/jUGfLo8uzq9sc3RjrypXpX6M1/t8tiAQASASEAtL3aZ9bvKebKun5+QW6fS0fO9cjh3PpRUS4WtTB+U13DY/IGgntZKgCglSxdrXeOBTsfeqtYriq7XtmT9QfncnOK+WLyu/wPvpE4Vl+zbuXmhucZY3Sm+4xWiiuayLABxq5Mv1sPCce+sOkhxhid6g9rNl3Uyk7WfAzEpeHPSYtX6sEvAGBXCAgBoI2lF+b182/9taavXpYkVcpVXX93QbZmdezFHrkfY12ouRufqFatqv/4yb0uFwDQKtaz9Q0qujbuHkzny5Iav0GJtVbzufkHpxffFR6sb3Jxt7NxA0eiRxT3xZUr5xpaV1spF6Tpn9fXHOzo3fLQk31hOR1GH83ucJrw8OfqU8evv86GJQCwSwSEANCmcumUrv3sJ5K1mvzwfS1PTermhUUVc2UdPdcjf+jRH9oq5bLmb36i+MCgAuHIPlQNAGgJy5/Up39uuv7gnR2MA40NCJPFpErV0sYBocMhdR6VkjelamXD850Op/7R8X+kU4lTDa2rrUy9I1VK0ujm3YN3BTwuHeoK6spcRpXqNjcrkeoblhy5u2HJuzsoFgBwFwEhALSh9XxOV974oZxut07/6lcUjHXq/Ld/oKXJBY0+k1A44X/0IJIWbt1QpbSugRNP7XHFAICWsvxJfefiYNeGb6fyJTkdRmF/Y3cLns/NS/rM+oP3Sxyrh1fpzacQN3rTlLZSytV3Fu4+IYW6H+uUp/sjKpSqurW8w67NxJH6+pK332TDEgDYBQJCAGgzlVJJV974kaqVik6++gvyhzoUH3hO+UxV69mPFe1+vA9rtWpVc59cUaS7Vx3xh3cnBAC0qXJBSk3UpxdvErYlcyVFA245HY0N4+Zycwq4Agp7whsfEButd51tMc0YuzD5tlSrPFb34F3D8YA6fK6dbVZy170NS36w8zEAoM0REAJAG6nVqrr21hsqZDM6/sprCkZjSs3nNHezoKMvvapAxKGrb/3koY1LNrI0Ma5SsaCB43QPAgDus3xdsrVN1x+U6h2EsQZPL5bqAWFvqHfzLkCnS+o8XK+xtoMprdjcelaavSh1P1XfnOYxORxGp/ojmljJa/XO2pTb5o9JIy9Li1el5PjOxgCANkdACAAtolatKjU/+1jh3Uastbr57jtaXZzX4bMvKdLdo6WpNd28uKRgxKOnvnBMR1/4nNaWl3Trwruy1m4+Vq2mmWtXFIp1KtKz9QLkAIA2s/yJ5AtLHRusAyipWrNK58sN36AkW8pqrbS28fqD90scq0+FXZtt6PXb3uTb9dB19NVtn/r0QFhOY/TWrZWdX3/oc/Wg8Prrm64xCQDYHAEhALSAWrWqqz/7ia688UNdev3fKr0wv+0xJj+6pKXJcQ2fOq1Y/4huXFjU+PtLCkY9OvpCj5xOhxLDoxo8+YwWb9/U3PVrm46VnJ1WMZvRwImnWKsJAPCpynq9gyux+fTi1UJZ1ZpteAfhXG5Okh4dEMYPSw6ntLT5cw7bVMxIs+9JvU9Lgfi2T+/wufX8cExX5jJazBR3VoPTJR39spRfYcMSANgBAkIAOODuhoPp+Vn1HzspW63q45/8QNfeelPr+cdb0Hv+5nXNXL2snkNHFIyP6aMfzSi9kNfQybhOfK5PHp/r3rFDp55RfGBIty9dVGr+4e4Ka62mr1yWLxRWvH+wYfcJAHgCrNysr0G3xfTiezsYN7iDcC43J7fDrYT/EdNb3T4pOnJnKvTm3fLYhsm36tPKR17Z8RDnRmPye5z68SdLW85i2FLnYanrmDTxplTcxZqGANCGCAgB4AC7Pxw8fPYljZ4+o+d+9asaOvWsUrPTeu/vvq3pKx+pVt182nFydka3Lr6rSHe/HK4xXX93QS6PQ0+92q++I1GZzywQb4zR0RdfVjAa1Sdv/1T5zIP/wF5dmFcundTA8ZMyDh4jAID7LF2VPEEpsvkPkFL5ekAYDTR+B+OeYI8c5jGeTYmjUiEl5ZYbWkMry5fzKlQK2z+xkJbmLkl9p+tTfHfI53bq5UOdmk4Vdr6jsSQd/lL91xtsWAIA2+F69CEAgGb4bDjYc+iIJMnhdGroqWfUNTKm25cuavKjS1q8Pa6x588q1tv/wBhryWV98vabcnk7VC6Pamkqp97DEQ0ej8nh3PwDlNPl1vFXXtOHP/iurv70J3rmS78it8crSZq59rE8Pr+6Rsb27uYBAK2nWpaSN6XeZx+YXjy5ktfv/+m7urWU06GuoH73lVGFvC753M6GXbpULWmlsKJzvece74TEUen69+rrJYa6GlZHq5rKTOnb49+WtVZRb1R9wT71BnvVG+xV1BvdejmRiZ/Vfx15eUfXttZqIb+gycyk1k1Jac3rT997X68dT8jIqmqrqtmaarZ27/cRb0Tnes4p5Ak9PKA/Ko18Xrr143pHa+fhHdUFAO1m1wGhMWZI0v8uqUeSlfRNa+0/3+24ANDONgsH7+cLhnTildeUmp/V+HsXdOWNHyreP6jR02flC4VUzGZ15Y0fK7daUzB2VJJTJz7XpXDCf2+M9MK8vvXHf6Tk7Izi/QP6zW/8U0XvbDriC4Z0/JXXdPnH39cnb72pp77wi8qmk1pdnNfo6TNyOBv3wQ4A8ARI3qpvDpE49sDLv/+n7+rmUlY1K91cyup/+sF1fePXTjT00gu5BVlZ9QYfc+Msb4cU7peWr0mjn29oLa1mubCs7058V3FvXEdiRzSfm9et1Vu6krwiSfK5fOoN9Kov1KfeQK+6Al1yOe58jMwnpfkPpYEzki/y2Ne01mq5sKzr6eu6mb6ptdKajIxcDpdCHRVdmVvTu9NJDcVCMsbIaZxyGMe9r2vJa7qeuq6zPWf1bNezn9Zz1+CL9bpufL8+ndxJXwwAPEojvlNWJP0Ta+1FY0yHpAvGmNettR83YGwAaDuPEw7eL9bbr8iv9Gj2+lXNXLms97/7bfUfP6m5m7e0OJ5WfOAFdY90avjpuFyf6db41h//kZIz07LWKjkzrW/98R/p9/77f3Hv/XCiS4fPvqQb776l25cuar2Ql8vjVffY1jUBANrQ0jXJ7a8HMve5tZRT7c6ScjUrLWXXFQ82dnrxXG5Oxhj1BHoe/6TEMenmD+tTZP3RhtbTKrKlrL5z6ztyO9z6yqGv3OvIs9YqtZ7SfG5ec7k5zefmdTtzW5LkMA51+buU8CfUOf+xOqt5dQ6c0eOsKJkqpnQjfUPXU9eVXk/LGKOhjiG92PuixiJj8jg9stbqby7OaDm7rn9wdHTDTtPV9VX9bPZnenvubV1JXtGrA69qJHzf3zunSzryy9IHfynNXJCGX2rAnxYAPNl2HRBaa+ckzd35/Zox5oqkAUkEhADaUrVSUXp+Vuv5nOL9Q/KFNpj+sonthoN3OZxO9R97SqHYgG6eP6/LP35X+UxFfcde0vGXD6mzf+MakrMz9xYCt9YqOTvz0DHdo4eUX01r9pN6J8HgyWfkcjf2gx0AoMVVK9LKdanrhPSZ9WkPdQXvdRA6TH3twb3YwbjT1ymPcxvj3g0IV25Ig485NfkJUqqW9J1b31G5VtZvHvnNB6brGmMU98UV98X1VOdTkuprFM7n5zWfm9dCbkHXFy/p8szb9U7MT/5vdXg61OnrVKe/U52+TiX8CYW9Ya2V1nQzfVM30je0XFiWkVF/qF+nu0/rUOSQ/C7/A3UZY/Ta0YT+z59P6t3bSX3h6MNTwCPeiH597Nc1mZnUGzNv6Du3vqOR8IheHXhVEe+dTsbOw1J8TJp6u97h6OTfLgCwlYb2WhtjRiU9L+mdRo4LAAfd3VBweWpSqbkZ1aoVSdLtSxcViifUNTKqxOCI3D7fpmNsJxwsl6rKr5ZUWCspn6l/FdZKsjUr6ZBCiZgGTgZ04pWjD+xQ/Fnx/oF7HYTGGMX7BzY8buSZ51TIrCqzsqy+o8c2PAYA0MbSE1KlVA8IP+NPfveFe2sQDsX9+oXj3Q3dwbhaq2ohv6CT8ZPbOzEQl4KJeudjmwWE1VpV3739XSXXk/rq2FcfvfOzpIA7oEORQzoUOSRJsh/9rbLRrFae/ppWqnmtFFa0UlzRRGZCVvUfPjqNU1Vb30itJ9Cjzw98XkeiRxR0B7e8VnfYp5N9Yb03mdazg1FF/BuHe8PhYf1O6Hf0wfIHOj9/Xn9x9S/0XPdzOtt9Vm6nu74W4Xt/Js2+Lw29sJ0/IgBoOw0LCI0xIUl/I+k/t9ZmNnj/65K+LknDw8ONuiwANE09FJzTyvSEUnOzqlbKcnt96hoZU2JoWN5ASMvTE1qenND4e+d1+/2Livb0KjEypnj/gJyuT/+x+6hwsFKuKjmbU2o+r3ympHKxcu89t9elQMSjSCIif9ijQNgjX2hMDscWC4rf8Zvf+KcPrUG4EeNw6MTnv6hKuSS3d/OQs5XwXAKABlq6Krk8D00vlqThzoBe/4MvSpI+mE7rB1cWFWtgQLhcWFalVlFfsG/7JyeOSZNvSaW85Ak0rKad2o9nk7VWP57+sabWpvRLw7+kofDQ9gfJLsksX1PH0MvqSJzU6H1vlWtlpYtprRRXtFJYkd/l1+Ho4U87+x7TK4c7dX1hTT+7saxff2bz/7dOh1PPdz+vY7Fjemv2LV1cuKhryWt6pf8VHYkekYkO17sI+59nLUIA2EJDvkMaY9yqh4N/bq39fzY6xlr7TUnflKRz587ZRlwXABph/sYnSi/MyenxyO32yOXxyuXx3PnyPvCrcTiUXpjTyvSkUrMzqlbKcnm8SgyPKjE0rHCiW+a+qVWDJ05p8MQp5dIpLU3e1vLkbaXe+amcLrfi/YNKjIwqnOjStbfefCgctDWrzEpBy1NZpeZzqlWtfCG3Ign/vSAwEPbI7d35ZiHRnt4H1hzcinE4nphwUOK5BAANU6tJy9elzqOPDGCSuZI8Loc6vI0LauZyc5KkvtAOA8KJn9WnGfc927Cadmo/nk3nF87ravKqXuh9QSfiO9ws5vYb9Sm7Qw+v7ed2uNUV6FJXYHe7Q3f43DozEtM7t5J6fjim3sjW/wYJuoP65ZFf1qnOU3pj5g29PvG6Plr+SC92HVb/JxMy85ekgbO7qgkAnmSN2MXYSPoTSVestf9s9yUBwP6ZvvKRJj+6JF8wJGutKqWSqpXyI8+rh4Ij6hwcUaTrwVBwI8FoTMFoTCPPPKfM0qKWp25reWpSS5PjMg6HbK12LxwsZEtanspqZSarUqEil8epxFCHEoMdCkY9qn/bBQDggEhPSOWC1HX8kYem8iVFA+6GPsvm/3/27jNIkvM88Pw/M8t7213tvR/vB26AAQGQBA1AcqXTxip292JPcacNhpZSSLFxH06huC8bjNVdKBSr3biVVivtSiIlkgBBEiQcYQeYwXjf093TvrqrTXX5qiyXeR9qpgeDmemuNoNx7y+ioqqrM7Oy2lRlPfmYTASXybVq2eptOUNgccHi0H0RILzbBpcGOR45To+vhz216yyrTs1VyrJbH7/rWZe7W7xcCCf4YGiBf7ansaq/mzpHHd/p/g6Xo5c5FjnGT9MzeErz9F95lZ5gD1ZT9b2hBUEQHiWbceruceC3gfOSJJ25dt//qev665uwbUEQhLsmPHiJyQtnCTa30bn3wHKQTyuXKRULlAoFSoX8tevK7XKxiNMfxFVTgyyvPXNPkiTcNbW4a2pp27GHWGSG6PQkTl8tklLDpY9mSMdUJFnCHbTS3O/HU2tFVlYOQAqCIAjCPbM4VMkc9LWvuuhSpkiDZ/Oy0XVdJ5KJ0ORcR5ksgCRVsghnzlR6KBo2d3jK/WQqNcW7U+/S6Gzk6can1x+knf608nNq3Le5O3gbZoPCwfYAb1+eY2Q+TVets6r1ZElmIDBAt6+b0fgoF1H4ePx9jn76H+loepwB/wB19jpx0lUQBOEzNmOK8UeAeGUVBOGBMjN0mYnzpwk0tdwUHITKRGCTYsVksa6wherouk65pFHKaxQLZUqFMqXCZ27nLeh6O+FhFa28iNVpoqnfj7/BvuJwEUEQBEG4L+h6JZvM17HqlNhiWSOZK7Kl3rVpD58sJMmWsoQcofVvJNAF0ycgNlZVFuSDKJqL8sb4G3jNXl5ofQFlHSc5gUqvxvnLULcDjF9M25GBehdnpmJ8NLJIe9CBUkWP5euMspEeXw893m4W8yUupacZSowzHBvGa/HS7++nx9uDxfDwtFARBEFYL/HpUxCER87syBXGz57C39hM177HVi0PXqtsskBkNEFiIUepUL42WfhWsiJjMMkYzQrBZheBJgc2lyghFgRBEB4giWkoZG47vfjzYtkCwKZOMF7uP7ieASXXuZsrwa7FoYcyQJgupPn56M8xykZebH8Rs2Je/8Zmz4JWrgz8+ILIssSTXUFeOR3m7HScXc3etW9Ekgh0PM9TF37MwcAeRswWLkUvcSR8hKMzR+n0dNLt7cZusmNRLFgMFmRJVG8IgvBoEQFCQRAeKZGrw4ydPoGvvpGu/ZsXHNR1ncR8jshYguRCDlmR8dbZMFkMGM0KBpOMwaRgNF27bVZQRNmwIAiC8KBbuAKyAfwdqy4ay1R6/G7mBOPZ9CxmxYzXvI6g0XWyXBmwsjhUCX6tN7vuPlQoF3h97HUK5QIvd72M01Rdie5taRrMnAZPMzg2NoBkrVoDdlr8No6NLtFf58JiXMfvKNAFjiDGqeP07f039Pn7WMwtcil6iaHYEFdiV25a3KSYloOFFoNl+bZZMdPmbiNgDWzSsxMEQbg/iAChIAiPjLnREUZPfYq3roHuA0+sq4fg55XLGkvhDJHRBLlUAaPFQGOfj5pmJwbTw/MBQxAEQRBuoeuweAV8bWBYPSttKVNAksBjXbkUeS1mM7OE7KGNZ98HuiFyHuKTlefzkHhv6j2iapQX217ceEBraRTUBKrUTvbv/g5zRyeW/j4U1+aVjK/kya4gf3dsgmNjSxzqXkeAUpKg5XG4+CosDEJtPwFrgKcan+Jg3UEimQi5co58KY9aVlFL6vJ1rpQjpsbIl/MUygVOzZ3i6aan6fE9fBmngiA8ukSAUBCER8L8+ChXT36KJ1RPz8EnkZUbwbtioUxRLWO0KBiMclUfMgpqiYWJFHMTSUr5Mja3mfadNfjq7chr6I0jCIIgCA+s1CyoSWh9sqrFY9kCbqsRwyZl0OdKOeL5OL2+1cubV+VrqwxaWRxGNQVRDAaMlge/L93u2t20ultpdjVvfGPhkxSzEunLo0gWK9kTJ8ieOIGpuQlLfz+mtjYk5e6dHA06zQzUuzk7FWd7oxuPbR2ZqMFesAdg4gjU9FWChoBRMdLkqm7QTbaY5c2JN3ln8h1i+Rj7Q/tFexhBEB4KIkAoCMJDb2FijJHjR3HX1C4HB3VdJxVVmZ9IEYtklvsESrK0XBZstCgYzUrla4uCyaIgyRLR6TTRcBqtrOMJ2Qi1uXH6LQ/1waGu6+SHhiiMjoIkIxkUkBUkRQbFULmWlWv3y0gGA6bWVhTnBkqZBEEQhPvbwmClHDfQVdXiS5nCpvYfnE5NA1DvqN/wtnTZQFz3E/noPWLORRr7Bmjesn3D273X/FY/fqt/4xvKLqHNXiF5pYTkC+H9jd9AL5VQL11GvXyZ5C9/hWyzYu7pxTLQj8G7gZLvFRzs8DM0l+K9Kwt8bVvd2oPNkgTNB+Hyz9bdc9JmtPH19q/zwfQHnJo7RVyN82zzsxhXGdIjCIJwvxMBQkEQHmqLk+MMf/oJrmANvY8fQtcl5saSzE8kyaUKKEaZmlYXDo95OZOwqJYp5Euo6SLJxRzlonbTNmVFJtDkpLbNhdWxeR907lelWIz0e+9TnJ5GdjqQFAN6uQRlDV0rQ7mMXi7D54axGBuv4nn5pXu014IgCMJdpeswPwjeVjBaq1hcJ5Yp0OyzbdouTCQnsBgs1Nhq1r2NYiHPwvgokavDqJEExkSSxi0haturC3o+KvTpkyRPT6E7BnB/5avItsrv0X5gP7Z9eylOTqJeukTu7Blyp09jrK/HMtCPuaMDyXgjcKaXy+iqiqaqy9fXb6PrWLZuRTbfuVzdYTbweGeAdwfn+fGpab62rR67eY0faWv6YfyjShZhoHs5i3AtFFnh6aan8Vq8fDLzCa+MvMJX276Kw+RY87YEQRDuFyJAKAjCQys6PVkJDgaCNG89wOTFGNFwBq2sYfeYadsexNdgX3VYSLmsVQKH+TKlQhmHz4LxEegvqJdKZE+dInfyJCgGHE8/jWXLwB0zJXVNqwQLNQ31/HkynxylODuLsW4DkyUFQRCE+1MqAmoCWh+vavFkrkRJ0zctg1DTNSZTk7Q4W9Y1bTYTjxG5OsTCxDhauYQzEKTp0Ffwj7+KHJDBtnmBzAdeuUjm3V9RzJlwvvQCxtqbA7KSLGNqbcXU2oqWyaAODqJevETqrbdJf/AhituNrubQ1Dx6obDiQxUmp3B/4+tIhjt/TN3R5MFmUnjzYoR/+HSSr2+vp9a1hnJwWYaWgzD4OkSvQqCz+nU/Q5IkdtTswGP28Pbk2/xo6Ed8pe0r1Npr17U9QRCEe00ECAVBeKCUCmXKZR1JAlmWkK5fJG4KXCUXFxj85CMkyYEudTH48TyyIuNvsFPT4sLuWb2Z+nWKIqPYZSz2e1M6oheLFCYnyQ+PUJqfQ/H5MdaFMNbVYaipWfEger2K4TCp996jvBTD3NWF/YknUBz2FdeRZLlSXgxYt20jd+YM2ePHcX/jG5u+f4IgCMI9tjAIklzJwKrCUrYSGNqsCcbz2XnUkkqLq6XqdTStzNL0FJGrwyQX55EVA4HmVkIdXTi8vspCyWZYHIb2pzdlPx8G6tFfkRuaxnr4O1j6+lZcVrbbse3ejXXXLorhGdTLl9BzOSSvB9liQbJYkK1WJLP5pmvZYiE/OkrqzbdIvfNrnM8/t2Lrlu5aJx6bkdfOzPCPx6d4bqCW3tAahqXUboHxI5UsQn/HurIIr2t1t/Jy58u8PvY6r468yuHmw3R5RQaqIAgPHhEgFAThvlUuamQSeTLxPJlEgUw8Tz5bvOPynw0Wzg4doVhQqevcja4baN7iJNDowGB8MDL/PhsULIyPoxeLyFYLxvp6StElCmNjlQUVGUMwiLGuvhI0DIWQ7SsH8laiqSqZjz9GvXgJxeXE/fWvYWptXfN2JJMJ686dZD7+hOLcHMZacTZdEAThoaHrsHAFvC1VlRcDRNN5AHzrGSxxG+PJcSRJqnqwRCwyw9XjRymoOSx2By3bdlHT1o7R9LkThoEeGH4TMlGwb0LvvgdcaWGB9K9+hrEuhP2Fl6teT5IkTI0NmBobql7H0tODlkqR+eQoitOB/bHHVly+xmnhn+9v5ufnZvnl+QiLqQKPdfirGxYnK9B8AIbegNj4hidX+61+vt31bd4Yf4O3Jt4ipsbYG9r7UPenFgTh4SMChIIg3Be0skY2WSATL1wLCObJpYuVDyGA2WbE7jFT0+LEYKoMGdHKOrqmV25rVG5rOksz40hylvade2jb3vzADBC5U1DQ3N2NuasTY0NDJUsP0HI5irMRirMzlCIR1PPnyJ0+DYDidmOoC2EMBpHdbhS3G8XlWjHT8PoQksxHH6GpKtZdO7Hv23dT36C1smzdSvbUKbLHT+D+2ovr3o4gCIJwn0nPQy4GzfurXiWSVHFbjVg3qUXHRGKCO0ZMqAAAIABJREFUOnsdZqW6ioCpC+eQFYXex5/GG6pbfj+9RaCzEiBcHAL7wU3Z1weVlsuRfOUHSFoO1zf/5V2pWPg86+7dlJMpsidPITtdWLduWXF5m8nAt3c18t6VeY6PLxHN5HlhIISlmhPCoW0w8XEli3CDAUK4Nryk4+u8P/0+J+ZOEMvHONx8GKMshpcIgvBgEAFCQRDuucXpFGNnF5cnCRvNBuweE756O3a3GbvHjNFc3QeKUrHI3OgETX3NbD28474PDOqFQiUoOHL1RlDQZsXc04258+ag4GfJVivm9jbM7ZUDWr1UorSwsBw0LE5Okh+8cvM6DgeKy4XiqQQMrwcPJVkm8/HHFCanMIRqcX/zmxgCgQ0/N9lkwrp9O9ljn1Kcn8dYs/4m8oIgCMJ9ZI3lxQCRhEq9p7psw9WkCimiapTH6lfOMLtOzaRJx6K0bN2Jr36VjDaLGzq/BJ7mTdjTB5euaaTefJPy7AiefZ3IbXu+kMeVJAnH04fQMmnS77+P7LBjbls5eKfIEs/21RJwmHnvygI/PD7FN7bXr17OrhgqWYTDb0F8clN+5wbZwOGmw/gsPo7OHCVZSPKNjm9UHcgWBEG4l0SAUBCEe6pc0pi8tITNZaKu04PdY8ZkUdYd2AsPXqSo5uh97Kn7NjioFQoUxsYpXB2hMDmJXizdCAp2dWGsr79zZsMdSAYDxrq6awNBdqLrOnouRzmRoJxMVq4TCbRkksL4BFo2e/P6JhOOpw9hGRhY82OvxLp9O7kzZ8mdOIHxq1/dtO0KgiAI94iuVwKEnmYwVdfSIpMvkVJLaxsksYLJ5CRA1f0Hl8JTAPgaqytHpmnvuvbrYZI9epTC2CiOdgvGvgNg+OICXJIs43rhBeKvvErqjTeQX/7WLYNRbmd7kwef3cQvzs/yD8cn+eqWOloDq/yN1m2vZBGOH4EdmxMUliSJnTU7cZvdvDH2BifnTlYdzBYEQbiXRIBQEIR7am48SSlfpmtPLU7fxj44qOk0s0ODBJvbcPo3ngG3mTRVpTA2VskUnJqEsoZst2Pp68PU0bGuoOBKJElCstmQbbbbThHWCwXKqVQlaJjJYmprW3UIyXrIZjPWbdvIHj9OaXFxUzITBUEQhHsoswDZJWisPogWSaoAhNybEyAcT47jMrnwmD1VLR8NT2H3eLE6nJvy+A+7/PAw2ZOnsNRbsQY9UL/rrj6eruvksyUKagmHx4ysyEgmE+6vvUj8Rz8i+fOf4fnOd1Dc7lW31eSz8Vv7mnnt7AyvngnzZFeAXc3eO580VozQtB+u/hoSYXBX3zNxNe3udrq93ZxfOM+2wDYcJsembVsQBOFuEAFCQRDumVKxTORqAk+tbcPBQYCJ82dAkmjeumMT9m7jtEyG/OgYhdGrFKanQdORnQ6sW7dh7uzAEArdsyxHyWTC4Pdj8N/9BuzWHdsrE41PnMT15Rfu+uMJgiAId9HCYGXi6xrKi+cSKrIkUePceBZaUSsynZqm399f1XtoQc2RWlykaWDrhh/7UVCKRkm982uMoVoc9fNgawJHcFO2res6RbVMNlUglyqSSxWuXYpoZQ2otJmpbXNVek7b7bi+/nXiP/4xiZ/9HM+3v4VstaJrGrquIyu3bz/jthr5zT1NvHkpwgdDiwxGUmxtcNMTcmI23Gad+p0w+UmlF+G239iU53rd3rq9DMeHOTF3gqebnt7UbQuCIGw2ESAUBOGeiYwmKRXKNPR4N7yt5MI80ekJmvq3YrbZNmHvbqVlMpRiMbRsFj1fQC/k0fN5tHx++evl2/k8Wi4Huo7idmPbuRNTRyeGmuB9W/p8t8gWC9ZtW8meOk1paS8Gn+9e75IgCIKwXgtXwN0E5uqzoSJJFb/DhFHZeKZ8OBWmrJdpdbVWtfxSeBrQ8TdUWV4MaJpe3STch4ymqiR/8TqS0Yhzfw/S1RHoOryhbcbns8Qj2WtBwQLlorb8PaPZgNVlJNjixOY0oRhkFiaTTA8uMTMSJ9jkpLbNhfurXyX+05+S+MUvKO7awdTlSjsZX0MjwdZ2PDWhW6owTAaZF7fWcdGf5PRUnHcuz/Ph8CI9tU62NrqpcZpvHI8ZTNC0D0bf37RehNe5TC4G/ANciF5gR3AHHkt1Wa+CIAj3gggQCoJwTxQLZSKjCbx1lUEkG6FrGmNnTmKy2qjv6d/wvunlMuVYjFI0SmlhkXJ0kdJi9Ja+fQAoMrLZjGQyI5lNldtOJ5LJjOJ0VEp3/f6HJiio6/q6not1xw5y586RPXEC1/PP34U9EwRBEO66zGLl0lX967iu60SSKt01m1PeO5GcwCgbqXPc2j7jdqLTk1gcLqyu1ctTAQq5EkOfRqhtcxNsfnRKknVdJ/XW25RTSTwvv4yy+FGlx+QaMkU/b3E6xeiZRRSDhM1lwt/gwOo0YXUasTpNGG8z0dpXbyebLBC5mmB+IsnceBJfnR1Dz3bC7/yUwvBFvLv34q2rZ3FqgsWpCUxWG8GWNmpa27E6XcvbkiSJLQ1uBupdRJIq56cTDEaSnA8nqHGZb84qbNwL4VMw8jbs+lewiW1fdtfuZnBpkGORY7zQKiopBEG4f4kAoSAI90RkJIFW1mnchOzB+YkxMvEluvY/jmJY28uaXihQnF+gtDBPaXGRcjRKaWkJrpW6oMgYfH5MLc0YAgEUvx/ZbkcymZHNJjAYHprg353ous5YcoyTcyeJq3F6fD1sDWzFa6n+dyfbbFi2bCV35gylvXsxeDf+excEQRC+YNfLi4PVB43i2SL5orYp/Qd1XWciOUGTswmDvPr7fbGQJ7kwT31PX1Xv1dlkgaFPI5RLGibro/UxqTQ/T2F8HPvjj2H0WmFoFFoeA/n2ZbyriYbTjJ5ZxBWw0LW3FmUN2aM2l4n2nUEae71MXJjiytH3yMbmMVvraSwWabc4ce3aS+v2XcRmw8yPjxIevER48CLOQJCalnb8TS0YjEagEiisc1upc1t5qjvIlUiK8+HEclZhd62TrQ1uajueQbr0GkTOQf3mtauxGW1sD27nxNwJ5rPz1NhWH7giCIJwLzxa73yCINwXCmqJufEkgWtnkjeiVCwyeeEsDl+AQNPK0wx1Xaccj1OKRChGIpTm5igtRisTGQHZbscQ8GNtasIQCGII+FE8HqQ79Lh52Gm6xmh8lJNzJ4mqUdxmNy2uFi5FL3Fh8QJNzia2BbfR7Gyu6oOXbecO1PPnyJ08ifNLX/oCnoEgCIKwqRYGwdUA5uoz664PKNmMCcZRNUq6mGaPa09Vy8dmwui6VlV5cXIxx/CJOWRFpvdg3YarGx40+cFBJIOCZWAApo8AEtStL0i2NJNh9MwCTt/ag4PL+5PNMHnxHAvjY/hDBpr791MsBsgOjXPh15PUpE5gbmtH0+xYXFvwG9qJz00xMzTB2JkxJEnG7qnDEWjC31CHr86OO2jFYlTY3uRhW6ObuWSe8+EEVyJJLoQT1DodfNlYg2/sfQj2gnFzhuoA7KjZwYXoBY7NHuPrHV/ftO0KgiBsJhEgFAThCzczHEfXdeq7N96HJTxY6UPT+9hTSJKEruuVgJ+uoxcKlObmKM7NU5qLUIzMoefzAEhmM8baGmx79mAM1WKoqUG+S70LHzSarjEcG65kDObjeMwenm1+li5vF7Ikky1muRi9yMXFi/xi9Be4zW62BbbR4+vBpNw54Cvb7Vi2bCF37hy2vXurmkYoCIIg3CcyUUgvQNdza1otklQxGWT89o2dEIRKeTFAi2vlE4LXRcNTmG127N6Ve99Wst0WsNiNdO8PYX7Esgf1Ugl1aAhTWzuyQYbZsxDoAotr9ZU/JxbJcPX0PHaPme59aw8OlgoFwoOXmB0eBKC+u5eGvgGMJjO6phPt9zP+U5XpT69iy9sx2G1IsoQsS9jcLdg8LRRyCdLRKdJLM6RjYWIz9XhC/ShGBXfQWgkW1lgJuS2E3Bae6g4wOJvi+PgSP0r28UzmdZpGPsTSt7a/9ZWYFBO7anbx8czHTKemaXQ2btq2BUEQNsuj9e4nCMI9l88WWZhM4a8xoaRj5CMpyskkei6HXiotX1i+XUYvFaFcRi9e+56uoWsa+UKBkalRPDY76kICVdOXswFvIkkY/D7MnZ2VYGAohOL1PvSlwWtV1spciV3h9PxpEvkEfouf51uep93TjizdOMC3GW3sDe1lV80uriaucm7hHB+GP+RY5Bh9vj68Fi///oN/z2RqklZXK3/+7J/T5Kxkb1h37iR3/jzZk6dwHn7mXj1VQRAEYa0WKgGbtfakm0uoBJ3mTRn6MZGcIGgLYjfaV122VCySiMxS29G14vt9ZDTB5MUoTr+Frj21GG7TF+9hV5iYQFfzWHp7YP4yFFVo2LXm7cTnsoycnMfmMlWCg4bqg4OlQoH58atMX75IqVAg2NJK08A2LPYbw3AkWSLQ6ML7L59i6X/8T0ymUdwvvHibrTUCA5RLJaYunSc8eAmXP4rV2U4skiU2m0GSJVwBK96QDU+tje1NHvrrXRwfczF46hJLR9/CqrSzpbN90wbWbAls4ezCWY7OHuXbjm+L41BBEO47IkAoCAK6rqMlEmiqil4o3HTRCgX0QvHGfcUCaBqS0QhGI5LRiGS4dn39YjIiGQxIBgNaNks5lUJLp9FSKSbGi2RjGvWeReJHb0yyQ5Er2zEYkAxKpbefwYCkGJAtFlCUa49jAElCkmXCV69grK2lbfsezBZLpaG0JINEZZqdomAI1mCsCSKZNp658LAqaSUGlwY5NXeKdDFN0Bbky21fps3VtuLBqyIrdHu76fZ2E8lEOL94nnOL5/j7y39PPB8HYCwxxnff+S6vvvRqZR2HA0t/P+qlS9j27kFxPjoN4AVBEB5oC4PgblhTVllZ01lI5dnetPGKgVwpx1xmjj2h6sqL47NhNK2Mv/H25cW6rjN1aWl5YFrHziDyJkxZfhCplweRbTaMzc1w+m/BHgBPdVma1yUWcoycnMPqNNGzP4TBuHqgVdc04nOzLEyMsRSeRtPKeGrraNm2E7vn9r2K43MRXv3+n7AUnsZlsfHNulqCu2//N6EYDLRs3VEJPo4N493lZcdznaRjeWKRDLFIlvFziyBJOLxmfPV2DrT7Sfi/RfjNP2PyxC84u/RNDvfW0OCxrunncTsG2cC+0D7enXqXscQY7Z72DW9TEARhM4kAoSA84srxOOkPP6QwPnHHZSSjoTKl12RCMhlBktAzGSgW0YvFSqZfsXTTOumCkQ8nmkgXzDjNeZ7qXsTssZLU3YT6rfh6epCdTmSHA8XlQjKb13QmNbkwT2Z+mrYDB/H1b13383+UqSWVi9GLXFi8QKaYIWQPcajpUNU9BT8rZA8Rsoc4WHeQ/3Lmvyzfr6Exnhy/aVnb7t2oly6RPXkS59NPb8IzEQRBEO6q7BKk56Hz2TWttpjOU9L0TRlQMpmcREdfQ3nxNEaLFacvcMv3tLLG6JlFlmbS1La5ae73IW1SltiDRstmKUyMY92+AykdgeRsZUr1Wo7JFnMMH5/DYjfScyC0ahZmJh5jYXyUhakJimoOg8lMTXsHwZa22/6+PuvV7/8J0fA06DqJXIbX/uL/4X/9//7HHftFS5JEx669FNUco6eOYzRb8Dc24fRZaOrzkUsVic1miM1lmbwQZWEiRdv2AAMHv0L9uV/xdmKUfzyep7/exROdAezmjX187vH1cHr+NMcix2h1t95UoSEIgnCviQChIDyi9FKJ7MlT5E6dBFnB/thBDH7/tSDg5y7y6gcvuqZVAoWFIoWMymvfv4BaKAOQKlg4stjN/oPtWLxZ2g43YrKs/+VH1zTGzpzEbLNT39237u08qmJqjHML57gSu0JJK9HkbOLZ5mdpcDRsuNzFYXLQ5m5jNDGKTqXc22F0oOv68rYVpxNLb18li3DPHhSHY6VNCoIgCPfawpXKdbBnTatFEps3oGQiOYHNYCNoDa66bLlUIh6ZIdDcessxTKlYZvj4PKlojqZ+P6F21yNd6pkfGgJNx9LXC+FPQDFC7UDV66eWVIaOz2G2Geg5UIfxDsHBgppjcXKchYkxMvEYkizjDdUTbGnHW1+PXOW05KWZ8E3tZOLpJBf+8e/p+87/sjy1+PMkWab7wBNcfP8dho8dwWg5jCtQgyRJ2FwmbC4TDT1eYpEMExeiXDoyS6i1gwZfHd+SrnDM38/JqSRXF9I81hFgW4N73WXHsiRzoO4Avxr/FUOxIXp9vevajiAIwt0gAoSC8AgqjI+T/uADyokk+VAtb338DrH3fo6vvoGX/uiP8QRWPnt7O5IsU9Rkzn+0wJm3plAz5eXv6TrEI1miMxnqOtwbCg4CzE+MkYkv0bX/cRTDg/Uypus62VKWZD5JspAkkU+QKqQwKkZ8Fh8+iw+vxYvVsPFSls8/7nR6mrMLZ5lMTqJIlfLgbcFt+K3+TX2sP3/2z/nuO99lLDGGUTGSKCT40xN/yh/s+YPlD2G23btQL18id/o0jief3NTHFwRBEDbZwiC46sCytuFSkaSKzaTg2uD7flkrM5mapN3dXlUwLz43S7lUvGV6cT5XYuhYBDVTpGNXDf4GcYJKHRzEEAxicJjhwmUIba16em86pjL0aQSTRaHnQAij+eYgn67rxGbDzF0dJj4XQdc1HD4/bTv3EGhqwWhee+DYV9/AUni6MpSOSobgm6/+kPff/Dm9jx9i4NCzhDq7b/k7UQwG+p44xIV33+LyR++z9ZnnsLlvLn33huw4/RamL8eIjCWJ6Xtpld7j8cYJ+g7s4L0r87w7OM+FcILn+mvXHfhuc7dRY6vh09lP6fR0YpAfrGNZQRAeXuLVSBAeIeVkslJOPDqG5nSQ276FX/7PvySbqPSLi4anefX7f8K/+tP/vKbtFtQS59+bvhYYLNI84CcWyZBeUq+d5NWxOg1opQwOj5VoeIpSPk+xkKekqhQLeXRNw2SzY7bZMNscWOx2TDb7LWeDS8UikxfO4vAFCDStrT/OFylbzLKQWyCZT5IoJJYDgslCkpJ2oxxbQsJutFPQChTKheX7bQYbXot3OWh4PXBoMaztYLSklRiODXNu4RxRNYrVYGVvaC8D/gFsxrsztbnJ2cSrL71KsVzkB4M/4O3Jt/mbS3+DJEn8/u7fR5IkFLcbS08P6oUL2HbtQrav3nBeEARBuAdyMUhFoGPtg6Xmkioht2XDGXqRbIRCuUCrq7Wq5ZfCUxhMZlw1Ncv35VIFrhyLUC5p9OwP4Qps7om4B1FpcZHSwiKOp56EqaOgl6FpX1XrZuJ5rhyLYDAp9B6ou+Xkby6VZOz0CeJzs5WKj54+gi1t2FxrCzJ/3kt/9MeVHoQzYXz1DXztd36P6X/6IePZJBfff4ezb72Ot76RgacO09DXz9v/9T8tL/vSH/0xfU8+w4Vfv8mlD99l6+HnMdtuPv4wGBVatwXwNdgZP2vkymgXgQ/P0fyNPl7e2cDIfJr3hxb4pxNTvLitnrbA2o9fJEniQN0BXrv6GhejF9ke3L6hn4kgCMJmEQFCQXhILTdxngnjqa1j9+4DxAcvs5RKsFTME49Fb534q+uV0o0qfT4w2NTvY/vhAFZHgbnRFEdfu0w2kcFsBYvVQjxi5/JHNwe4ZMWA0WxGkmXy4Sl0Tbvp+waTGbPNjtlux2Kzo2YzFNUcvY8/dV+VBBXKBWbSM4TTYaZT00TV6PL3jLIRl8mF2+ymydmE2+zGZXLhMrlwmpwosoKu66SLaWJqjCV1afkyuDRIUSsub8tqsGIxWLAoFkyKCbNiXr7+7MWkmJjNzHJh8QK5Ug6/xc8zTc/Q5e36ws5UGxUjh1sOkywkcZqc/PeL/x0Jie/t/h6SJGHdvRt18ArZ02dwPPH4F7JPgiAIDwJd11lMFzAZZOwmBcO9HJ6xzvLifKnMUqZAd+3Gh1FNJCeQJZlGZ+Oqy2pauRIQamhcLlvVdZ3RMwtomk7vwTrsbvOG9+lhoF4eBEXG3NIA596slBbbfCuuUy5rxGYzTF5cwmBU6D1Yh8l647iiXCoyfekiM8OXkWWFth17CHV0VdWuphqe2tAtJ7Ktzz5Pw4mTfOnf/C5j41e59MGv+egHf3vTMkufOQne9+QzXHj3LS59+C5bnnkOo+nWvweX38rAoQZmvCUiR+IkfvoJLc88Tle9k3qPlVfPhHntzAzP9dfSX1/94J7rGp2NNDobOTl3kj5fHyZFDNMTBOHeEwFCQXhIvfL9P2FpegqA2GyYt3/+YwAcXh+1HV30t71AbXsn7/3tfyUemV3uEeerb7jjNhMLOX7xF2eJz2Wx2I2UiiXymTg1LTL9TxhR5BHCly8CoBiMPPZyL2a7g8hohmJeou9gM2a7BaPZgsFsxmi23FQirOs6RTWHmslQyGZQsxnymQz5bIZcKkk8MotWLlHT2rFiE2tN18iX85S0ElaD9a4ExMpambnsHNOpacLpMJFsBF3XUSSFOkcd+737qbfX4za7sRqsqwYzJUnCaXLiNDlpdjUv36/rOqliajlwGFfj5LU8+VKeTDFDTI2RL+cplAvLPf8+q8XVwrbgNhodjfckoNrgaGBbcBsALpOLv7741wB8b/f3MHi9mLu7UC+cx7ZrJ7Lt7mQ0CoIgPEh0Xefty5UyxuusJgW72YDDrGA3GXCYDdivXRxmA0GnGeVuDdlYGARnCKy3nyp7J/PJPLoOoU3qP1jvqK8qiJKcn6dcLNxUXhybzZKJ52nbERTBwWt0TSM/NISppQV58SxoZWi588m6TCLPwmSKaDhNuahhdZro2luL+VpwUNd1otOTjJ89RSGXJdjaTsvWHZgsdz9T03bthGPx0xNs+Y1/xtbDzxOfi/BXv/e/LZ8Mr+zfFGff+iVd+w7S+/ghLn34awaPvE//k4dv27JGUWSadrbh4ypjJycY+WQUb0sNLVv8fGd3Iz87O8sbFyPkimV2t6zt/wNgf2g/Px7+MWcXzrI3tHfDPwdBEISNEgFCQXgA6ZoGkrRiwCf2uUxASZL4rf/7PyIbDBSyGfLZLGo6Re/jhzjz5uvkUkksDidtO/Zw4mc/QTGZUAxGDEYjitGEYjTy4Q9HSC+V0CmSyqWR5Sw9+0M4vGYsVhcOfwMufxCnP4DV6UKSZVJLKkuzM3Ts8hHq9Nxhb2/so8lqw2S1Abc2IS+UC4wujhArp/k4/DFqWaVQLpAv52+6fLZUFypZd3ajHafJid1ox2F04DA5cBgdy18rskJZK1PUihS1IoVyYfl2UStSLFeu1ZLKbGaW2cwsJa2EhETQFmRnzU4aHA2E7CGM8u2bZK+HJEnL2YYrTW7UdZ2CVlh+/mpJxWly4jZvrJRnMxyoO8BkahKXyYXFYKkECSX43q7vYduzBy2TRcvnRYBQEAQBODMV50I4wfYmNzVOC+l8iWyhRDpfJpMvEU1nyeTLaJ+pAmj22fjWro0PmrpFLl6Zatv+9JpXjSQrA0o2OsE4kU8QU2P0+/urWj4ankQxGPHU1gGgazrTV2JYnSYCoufgssLEBFo2i6WzFWZeh5r+W7IHS8Uy0XCGhckU2UQeWZHwhuwEm504/TdKx7PJBGOnT5CYj2D3eOk+8ASuwOrDZDaLZDJhf/wxUm+8iXrpMtYtA3hqQ/gbGm/qVygrBt7+y//EO3/1n2ns30J9Tx/R6SmGjx2h5+CTd8xytG95nIH0BWZTl5mZc3B+MUdNq4un6zx8DHwwtEC2UOKJzsCa/gdr7bW0e9o5M3/mrrZ+EQRBqJYIEArCfa6oqqRjUTKxGOlYlHRsiUIuC4AkyUiShCTLlcu1oKGez2M2mlDz6vJ2LA4nY2dOLH9tslgx2ez4G5v56r/9fYwWK1q5RLlYpFQqUS4WKreLRfLZDOVikeTCJLpeRkJBkp0g17P368/h8AduW56h6zrTgzGMZgM1bWsvv7i+jfnsPINLgwzHh5eDfwbZcFNJrcPowG/xYzbcKLE1yAayxSyZYoZUIUUinyCcDt8SQJSo/Nw0XbvdLtzCY/bQ6+ul0dlIg6MBs3LvsxEkSVr+WdxvjIqRw02HeXXkVQ41HkKWZP76wl8jIfHvdv07PC+/dK93URAE4b4wEc3w/tACHTUOnumpuWOwQdN0csVKwHBkPs2xsSXGo9l19UNb0eJQ5XqN5cVQmWDssRmxGKubTnsnE8kJgKr6D+qaxlJ4Gm9dPbJSedzF6TRqukDnnlqku5Vl+QDKX7mCbLVgkiKglYlbu3j1D/4PlmbCuGvr2ffN75LP2dHKGja3mZYtAfwNdgyfmVJcKhaZvnSe2eEryAYDbTv3EGrfvHLitTB3daGev0D26CeYOzuQLZZb+hV+8w//L4pqjuFjR7hy9AjHfvJDkCQcHh9v/9VfUMjlbgzsqw3d2LjRitT+FPVDb+BrG2Ai4iNyNYGu6dTqkM+UOBmeITaV5vDOSgl7tX9r+0P7GUuMcWr+FE80PHGXfjqCIAjVEQFCQbiPXA8GpmNLZGJLNwUDASwOF65gDVaHE10HXdcqZ0U1DU3X0DWdYiRCdvA821u7uBCZIptM4AwEOfyv/3cCTS2YbXZMVuvygfNaDJ06Smw2g66DLEt4Qja8dXcuSU4uqqSiOZq3+FHW2D8pW8wyFBticGmQJXUJg2ygw91Br7+XWlvthsqGC+UC6WKaTCFDupgmXUxT0koYZSNGxYhJNi3fNso3Liblxv2PpMofHazjwL/OUce24DbOLpzlt/t/G13X+W8X/hs/Gf4JqUKKVlcrf/7sn9PkbFp9Y4IgCPcLTYNyAWQDKBs7rI5lCvzi/Cx+u4kXBmpXzESSZWm5xNjvMHNlLsVHI4u0+m2bm0W4MAiOmlX70t3OXFKlwbPx8tKJ5AQes6eqjPhUdJFiXsV3rbxYK2uEh2LYPWa8IZGddZ2mquRHR7H2dCGY/BUQAAAgAElEQVRFTlP29/HjP/sz4pEw6Drx2TAf/eD/5Sv/9j8QbHZic5tu+rvSNe1GObGao6a1g5atOzBaNl5Ovl6SJOF46kliP/xHsseO4Th06Lb9CgFqWtt57Df+BdGpCa4cPcKnr/4jWrkMrDCwr24HzJzCMvsePft+h7KukInnScfzeJdUjFdjjJ9f5CdXk/TWu3B6LTi8Zpw+C+6g9Y4BQ6/FS6+3lwuLF9gW3IbLtL4T6oIgCJtBBAgF4R7QdZ18NkMmFiMTj5FJxMjcIRho9/hweH3Yvb5bJvp+fpvZT4+THZ2ktrMX11e/whOuzT3IePF3ty/3IPTU2njxd+88dS2fLTI9uITJaqCmuboG5WWtzGRqksGlQcaT4+i6Tsge4lDTIbo8XZvWwNmkmPAplcnAjzytDLFxyKeglIdSDooqlK5dirkb95fyoJigcQ807AHT2j5s7avbx3hynPen3ucP9/4hvxr/FfF8ZYL2WGKM777zXV596dW78CQFQRDWKBeD8EkoZCsBwHLx2vVnbxfh+lR6oxW6vww1vet6OLVY5rWzM8iSxDe2N2A2VH8ST5ElDnb4+eX5CFfmUvSGNum9X01CIgxtT6151XS+REotUbvB8uJiuUg4HWZbYFtVy0fDU8iygreuHoD5iRSFXIm27cH7arDZvaTrOsnzQ8QzRpKJPBNLXrL+TuKRmc8Mr9PJJedp3RagVCySii6SvX68Go+RTSTQyiXsHh89jz2F03/nvtBfJEMwiHXrFnLnL2AZGMAQuPN+SZJEoLmVQHMrx1754Y1v6DrR8DTDn36M81rbHJvLXcmK7HwOzvw9TB1DaX0CV8C6PA27a28tJ4ejfHR+jiv5PFsLBtKjKjPDMVwBK+07gphttz+O3xvay1BsiOOzx3m25dlN/ZkI97/EfITI1RFcgSCeUB0Wh1O8Xgn3jAgQCg+15OI8i1OV0hQJ6UbfPkm69jXLZblc639n93ixud3Lk+82Stc0cqkk6djStYOrOJl4jFIhf20JCaurEgx0eH3YPasHAz9Py+dJvfU2hbExLH29OA4dQlrD+tVyB6388z8+cMfvq+kiS5EMsdkMmXjl+bXvrEFeJXswkU9wMXqRoaUhsqUsNoON7cHt9Pp6RRDvbilkYfYMhE9VgoPXSTIYzJUPuwYLGG2VzBGDtXJ/dhHGj8DUp9CwCxr3gbm6nk5G2cizzc/yyvArHJ09SqaYWf6ehsZ4cnyTn6QgCMIaFVWYOFIJDiKB2QmKsXJyxGC59rXp2sV443r+Elx8BaJbKkEEY/WBMU3T+eWFWeLZIt/a1YD7DkGElfTUOjkxHuPjkShdNc7NGViyXF689qBnJHGt/+AGB5RMpafQdO2m4V13ous6S+Ep3KE6FIORckljZiSOK2DFHbz7gzLuV2o6S2opQyZRWM54S58bhpIT21QEe9CLt8WJOxgisTBbCRJKEnaPl1O/fA01feMYwWAyY/d4CbV34vAH8Dc03ZNy4pXY9u8nPzxM+oMPcb/8UlWBFl99w41ehZKEw+sjHpllYWIMqAzec/oDlYtSh2PsCErNAEXJTCGXo6DmKORy1BRy7HJHOTUS4dh8if6AiUJCJTbjIjbbRte+DgKNjlv2yWFysC24jdPzp+n2dtPkEtUUj4psMsHgxx+il8tEpyufWc02B55QHZ7aOtw1tRhMYsK18MXZlAChJElfBv4MUIC/1HX9P2zGdgVhvcqlIpPnzzI7MoRiMCDJcuVN/zOTzHRdA51rjYtvnv4qyTI2l7sSrPN4sXu92NzeOwbtNK1MPpNBzaTJp9OomWuXdBo1lUTTKmULsqxg83jwNzYtb9vm9tx2clq1SktLJH/xOuVkAsehp7Bs3brms066rlMuaSiKvKb+PLquk0sViV0LCmaTld5+do+Zpj4f3jo7FvudP+jE1Tgn508yFKt8CGlztdHj66HZ2YyySQFa4XPS8zB9AuYuVjJfvK3Q9XylhMxorXzYXe3vJ70Akx9XgoTTJ6FuOzTvB8vq5V8he4jtNds5M3+GBkcD06lpNDRk5Kr6SwmCIHyepukUytrGet1pGsyehrEPK9nToa2VzDlzdRnwNOyGiY8rl/gk9H4NvHceLPVZH40sMr6Y5Ut9tTT51lcGK0kSj3cGePV0+NqAk5WHglVl/jI4gmD3r3nVuaSKLEkEnRvrizuRmMCkmKiz1626bDoWJZ/N0DRQyTacG0tQypdp3Lv26bIPk+M//4DIyAgABpOCQdHQF0axeWS0fJq0vov01Q9p37WbwSPvk0unsDqcbHnmOeweLzUt7dg8XuweDybrJpew3wWyxYJt/wHS771HYWQEc1fXqut8vlfhS3/0x7hralEzaVKLC6Sii6Sii0xdugDFMsxMwoXvVwa73BQglbBZLOyos3NuLsfFnI2DvU2kw5MsTBxlcfIijX39bDm0BZP15uPjPaE9jCXGeHfqXX6z9zfvy57SwuYqFvIMHvkAWZbZ+txX0HWdRGSW2Nwsi5PjzI0OI0kyDr8fT20lYOjw+u67oLzwcNlwgFCSJAX4T8BzwDRwXJKk13Rdv7TRbQvCeiTm57h64ihqJk1dZw/NW7ejGFY+G389YKim05Usv3icTHyJ2GyY+fGr15aSsDic2D1erE4nBVUlfy0ImM9m+GyQUZYVzA4HFrsDT00tNo8Xh9e3PNl3s+RHRki9/Q6S0YjnpZcwNty5H+DnFXIlklGV5GKOVFQlny0iyRJGs4LRrGCyGDBaPnutYDQbMFkU8rkSsdkMS7NZ1HTh2tlWM80Dfrx1dszWlV9altQlTs2dYjg2jCzJbA1sZWfNTuzGTW6uLlRoGkRHIHwCYhOVPlmhLZUyYcc6pgw6gtD/TWh9EiaPwszpSjZi7RZoPrBqr6p9oX2MJ8Z5vvV5fj35ayaSE8s9CAVBEFZTLGvMJVXCsRwziRwzcZVCSaOzxsG+Nh+1a81ai16Fq7+GzCJ4mqHzWXCGVl/vs2QF2p4Efwdc/hmc/YdKO4a2p1fsTXhxJsHJiRg7mjxsbdzY1PlWv40Gr5VjY1H66lyYDBs43sinIBmG1vUNTYgkVAJOE8Y19h/+LF3XmUxN0uRsquqk4dL0NJIk461voFgoM3s1gTdkx+G9d33x7gedewao72rG6jJiMCrkzp5FLSzhbssjB3ug4/DyslsPP4/Zbl/xpPiDwDLQj3rxIumPjmBqaUFaJQPrTr0KrQ4nVoeTmtZ2AEqFAumlKKlhP/rEUUyuJMaBFzE5HJgsNkwWy/Jxfl9S5ZXTYT4pwdcOb6E1Gmbo2DmunvyY6Utn6Tm4nY7dA8vZYUbZyOHmw7wy/ApHwkc43Hz4lv0RHh66pjH0yUfks2kGDj2LxV6pxrF2Ogl1dqNpZdLRKLHIDIm5CFMXzzN18RwGk5lgSxuNfQMYzY/2a5twd2xGBuE+YETX9VEASZJ+AHwTEAFC4QtVLhWZOH+GyMgQFruDgUNfwl1TW9W6lTJjBZvLjc3lJnCtkkXXdQq5XKVPYHyJTDxGeilKdHoCo8WKxe7AFQhitrdhuRYQtDgcGC3Wu3qGVdc0skePkj15CkOoFtdXvoLiWLnMs1gok1pUSUZzJBfVSmCPytlkp99CsMVJuaRRVMsU1RJqpkhqSaVUKN9+g5KEy2+hti2AN2TDZFn95SSai3Jy7iRX41dRZIXtNdvZEdyBzSgah98VRRUi5yqlcrk4WFzQ/jTU76hkC26UzQe9X4XWx2HyGMyerTxeTT80H7xj8NEgG3i2+Vl+MvwTfm/X74mDYEEQVqQWy8zEK4HAcDzLXDJPWauclAs4TPTVOTEqMufDCUbm07QGbOxt9dHoXeW9JbMII+/A0ihYvbDl2xDoWj2LeiWuetj9r2H0XZg6Dktj0PcNcN56PDITz/HO5XmafDae6l7HyZrPkSSJJzoD/PD4FGem4uxr20CLjoWhStXFOsqLdV1nLqXSU1tl9uWddiG3QKaYqW56sa4TDU/irqnFaDIzdWmJckmnoefRzh4EqOu8Ua6qaxpL736Ap8aCu9YAe79RqSB4yEiyjOOpJ4n/+CdkT53CfuDO7XHWwmAyVUo/Qy9BZxtc+SXEjkHDt285EVDrsvCbe5p45XSYV87O8dxAA0//ix5mhse58O4Jzr1zhNEzZ+jau4WG3l6sDiche4idtTs5NXeKdnc7re7WTdlv4f4zfvYUifkIHXsO4Arc+j8oywquYA2uYA1srQyyjM9HiM2GmR2+wvz4KI29A9R19axr8KQg3MlmBAgbgKnPfD0N7P/8QpIk/Q7wOwDNzav3ERGEtbiRNZihrquX5i3bVs0arIYkSZhtNsw2G776G9l5uqZVlQmoazpaWUc2SJsWMCzH46Tff5/C5BSWLQM4nnwSyWBA13W0kk6pWKZU0JavM/E8yahKNlHpCSgbZJw+C8FmJ66ABZvLtOK+lcuVoGFBLVWCh/kyikHGU2vDaK7uDWkhu8DJuZOMJkYxykZ21u5kW2CbCAzeLekFmDkFkfOVJvruRmh/BgLd65pAvCqLG7qfh5aDlbLjmdOVEubGPZXMhNtkftTaa5cPgjs8HbS4qivF20zifUl45Oh6ZfhGdqkSBJPkyv+npNy4luTK68T1+wzWu/O6sYpEVuXYuavM5hViRRldrwzjqHWZ2dnsod5jpcFjvamseF+bj3PTCU5NxPinE9M0eK3sa/XR8vnJvoUMjH8EM2cq/QM7n62UCG9WawuDCbpfAH8nDP4CTv1NJeO6af/yzzKpFvn5uRmcFgNf21a3OT0DgXqPlfagnePjS2xtcGM1reM5aWWYPl4JHNnXPnwili2SL2q3ZHIWC3lyyQROX6CqY6iJ5AQSUlXT7bOJOGo6RX13HwW1xNx4An+DHZvrwevddTffm4rhMFoyhrk2DsFdD2Vw8DpjfT3mnm5yp08j2x3INiuS2YxsNiNdv5hWPgZeUd21QX1XfgkXflQ5waDc/NnDazfxW/ua+dm5GX55PsJSu4+D3W3UdbYycmKMkZPnOP/uKaYvXSbU0UJ9Tx97avcwnhjn/en3CdlDWAwiS+xhMzc6wuzIFeq6eqlt66hqHaPFQrC5lWBzK429A0ycO83E/8/ee4fZdZxnnr+qOunGzgHdyIGZBDNFyVqJSpasHGzJ1o7GaR1le+1Ze2bH61mHCY/X4Rnba9nWruW4lpOy5JEsS6YCJSYxiQkEATTQaACdbvfNJ1XV/lG3EwCCAAGQlIS3n3qqzr2nzz3h3nOq3nq/9/vmg5w4sJ+t1+5leMu2F334/yV8a+B5S1Jirf0g8EGAm2++2T7L6pdwCWeFDarBcoVrXvkaN9NykfFMHVuXnTinPt+lseCKzgwIgedLlCdRvkR5AuVLPE8hvbX33DZ6voi92mhDvlAjm50lOzGLbrWwQuJd8RJkMEH+lRPkmUZnBmtO/WlJJSgPRmy+YtARgn0h8hwGIkpJVEme0Uvwmc7FbGeWB2YfYKoxRaACbh67metGrrvU2XkuqB2CD78HFvY7hcv3/y0M7lh732iY3+fIueUjID2XTXPyZqg+u3fTBUFYcQPtrbc7k/+j9zuFztVvO61icaUTfOf0nbz78nc/79+LS8+lS7jYMEY7A/tul7TbcaXTIUsSlO/jhyF+GOFHEX4Y4YUhfhTh+ecxaF2PtA2N49A8tlZn8bltwwtd2OzQbhjceWHUx2dArd7kq19/iANPPIlNY/qKAVfsvozLr9/L5tH+M4ashp7ilu2DXL+ln0dnXOjuxx6cYawaceuOAXYNFRDHHnDkoM5g4gYXQnuOGdnPGkO74JYfhac+CwfvdFYPV76JLOjjUw8fI9OWd944cX7eiafBy3YP89d3H+b+wzVevuc5KBOPP+yI5Gu/9zl9/mqCkl4G47jV4vj+J5k9dACjcwrVPrZcdS1Dm7ee8Xt+uHGY0eLoWU0m1mamAcHg5GaOPbWMtTB52bemevBiPpviJ55AxPOEwyXY9tzCx7+VUHrpy8imj9K6887TryAEIgiQYdAjDSNk1KsL0dpyVOjVETKK3LpKnUQSfuS0JGEhULzzxs184YlZ7jlYo9ZO+e6rx7nstl2M755g/71HWTx6gOknjrA4c5SB8U3cvutG/mnhC3x15qu8ZttrLu5JuoTnFY35OQ4+eB/94xNsv+6G57SNYl8/V778DpZnT3D4kQfYf89dHN//JNuvu/F5GQdfwrc3LgRBOAOsn9rb3HvtEi4BgCyJUb5/wbICr6A+d4Kn77uHpLOiGtx7Xsk+zgRrLbbbJa8toZeW0I064BSGuRE0m9BsW1pNS5q5/wkCSbVfEZYDbFDA+iHG9zEWdGZIu5pulqFzQ56Z1QQqADbP0fU6ZnkZXV8GrV1y2b4+vInNeCNDUCkipaBQ8fGDCBVIPF/h+RIvcESk5yuikvesWYQv5Hma7cxycPkgB+sHaaQNQhVy6/itXDty7SXD5dPh2Yg/cIP8v36nC4PDusySH34P/PQ9ENedCub4w269Qj/sugPGr7t4g95nQ1CEPa91yoSnPgcP/KUbaJ7kTehJj1dtfRVfOPIFOnnnO5I4Nkaj04wsTcjTtFdcW2fp6mtCSqJSmbBUIiyViUqlc7YyMMaQNuoky8ukjTrWGKqj4/il0pqS4jyUYtZa8tT0Ej+dGWm3Q57GeIGHlBKEQEqFkBIpJaJXVt47q+M0GnTqFHDrlXAXEW5SqE23UafTaJC0Wz0isEvS7ZDFMScnwZLKww8jdJ6ty2a/EUJIRx5GEWGpzMD4BAMTkwTRRnJOG8tCK+Ebh2v85088gmzPcmW5zXuv9Bmlhp/VV7ZIGg0RF8ZICqPowghj1QITfT5lX4A17vxZA1b32r26eQJqB2D2cXdO+zY7snBo93NKXvFMOHF8lq/f/QBTTx/EGsPmrZu59ZbryJcXmDt0gLm75/Euu4JNl135rN5ovpLcsHWA6zb388TxBvdN1fjSPfez2LmHncWYoa1XIne/+jmp484ZQRGufrtTVe//HPa+P+UudTvz3RHeev0kQ+VTn4vp9DTTP/GTpFNTBNu3s+WP/4hgy9lnNR0uh1y5qcpDR5a5fks/legcJvh05iZ4+iYdwfkcMNuICTxJEDfZ9837WDx6BCEFw1u3Ux0a4dj+J3nq7q9S7Btgy1XXMji5+ZTfeCfrMNeZ49bxW8/qMxdnjlIdHsFoj/npJiNbK+c8sfntDpOmpPufIiy1EWM3njbs/dsNqlxi8N++D9PpYJMEk6TYNMHGMSZJsL3l1XYSk9dq7v04htNMvK/A37KZ8itegXcWJKGSgtdeNcZQOeAr+xdodI/ylusnqA4VuP41u/jG/Tl/8+X7aB05SHHfMm848jq2Dkbsa32Tnf072dm382Kepkt4nhC3Wzz5tS8TFstcdtvLztuXvn9snL7XvIH5w4c48ujDPHrn5xmc3MK2a6+nUKleoL2+hO80iLPpyJ9xA0J4wFPAq3HE4H3AD1hrH3um/7n55pvt/ffff16fewkvblhrqc+eYGbf49TnTgDgBSFBoUhQKBBEBYJCAT+KnKlvwS1LpdB5jslz9GrJNiwbndNtNlg4MkVUrrL7lttO693wXPfbNJvopSXyWg29tIxeqvU6CwnGQm4kqfVpZwGtxCPOPBf6JAylIKMcpJT8jEDp09oYyWIRWSmjKhVkuYKqlBHlMoQFshMnyKYOk584DtaiSgWC7dsJtm/H37IF+SJMc2+sYaY1w6H6IQ7VD9HO2kghmSxPsrN/J3v69xCoF99+nzOMdgMnkzsFzZkI79oh+P++1xF6/Vvh9f8NikOQJ67oZK39L78KrVkciSCcCm/yJugsujDAziLk3dN/zmWvd+tXN8PY1TB5o1P4vJhCDJaPwKMfBawbJA9sP2UVa+0FC4sQQnzDWnvzuf7fhXguLc+eOCULYv+YS3RgrSU3OZnJqC8tcHz/kyzNHIUsRwIKgQIEtkfSuKI8hacURmuyNAXEyoEilXL3Us8jkIpACDwDeZyRdLuk3ZQ0jp1iLe6SJvEpKmMpBKWwQKVQpBIVKZTKyKgXghWshGEFyGIRVS4jy2V33yqXoFAgbuU0F2OatZjmYkyW5M94frKkQ6d+gm79BEln2R2GEk5ZvVrcslxpC40iJ/Cc4tpXEk+unS+FQWiwWUaeOb/UyM8Jw5woyIlCZ4mAEL0Q2nWhs0EJon7nPVfoX2sHpVN+Q9YY4naLTqNOt1Gn22i4drOB0WvHvPKcCwsF/F4dFIruWReEBGGIRGCNRXkKKyV5npHlOXmakiUxWRyTJTHH5ut84POP01peYigwXL+lnzSqMicqzJgys4nE784ylB5nzJxgnBpKGAA6okR5ZAu2vAnVP0k0MIkKQpbaKR++9wgL7ZSBos9b9k6yc6TElsEiWweLbB4oEHpqzbIid5NXUoLqzCKW9iNqB5yNATjSf0Vd2LflnEN0jdFMHzjEvfc9xNHpYyA8Nm/fyY3XXUO12Eca5wgpMHmH+cOPU587RliM2HrNtWzafdnZ+S7FDczTX6R26CEOtX0eK9xCuOkK3rx3gnK4NqlojSXuZHQaKd1mRreZErcylC/XJe9yybpWlv1Q4YUKYTQ2TbHGgNZYrV29srxSd+tM3/1x5hdmKd76fVx97XWraiQbxyRPP03riaeY+sCf0dCS1I9QWuMrycDNNxENVIkG+wkG+wiG+glHBmgudvjnvzpE2x+glC/xPT97I6N7d1PvZvzF16a4clOV1151DkTQkbvhwL+ir/0BdGkSP1LPen+uJ3W6veeUMYaP3fkIzE6zM8qRSlEYHCcqjZN1JGkrRyLImjWac1PoJKZQ7GNwbBfF6rD7LGuZ78xxIHuSN7/81YwOTbgJDN8/7b50mw0e/Oyn2L73JrqtQZZOdLjuVZvPyhv5+cBzfS7BhR0zxY8/TvMjf07/VQH+695/7ol4vsNgrcVmGbbbxcQJNokx3djVrRbdRx8DnVO46SaKN92EmHsM9v2T6+echiRcwYH5Fp999ASBkrz1+glGqxFv+/jbWDzRZHvtWqSBqLzAD259M4/OPYIZKvB9r/0xBvovKcO+lZFnGY/+6+dJOm2ue/V3X3ACT+c5x596kpl9j2O0ZnzXHjZfdc2lRCYvcug8Y/HoNJXhEQrl8/PtPVc807PpvAnC3sa/B/jvgAI+ZK39L2da/xJB+O0LawyLR48ws+8J2ss1gqjA2M7dIIQLsYq7ZCt1HGOtG8zYLMN0Opgk7XWuc8hdJ9tqDXnea+egDVZDf2WQ0eFJZBiBH2D9ALwA6/ngB+D5WOW7AYvWCJODzhE6B5316l47zyFPyZtdslSTG0mmJcaL0IUyOihh/AI2iBCFggtHkC5zb3W4QGUootwXuDGlcQMqFyZssUmCbrYwraYjH3tt3WhiWk1stnFA7Y0Mr5KC3tjYi9JPIjc5081pDtYPMlWfItEJnvTYWtnKjr4dbO/b/uJWC1oLScOFv3YWXZ22nfrIZGtEoM56y7kjbFYgBPhFl/QjrEBY7ZWKK3/1dhdKxnO8v07e7AjF0rAbgBeG4J4/gtbc2jal14tH14CA8WtdqNy2lzmFz8d+/MyqxOcT3SX45j86wnPPax2ReZHwQhKEf/LTP0hrYWF1WfoFRi7bixaGXGjyLCbvtDBpB7IWIrAI34PSMMLzexyWQgmJLxSeEngmRy1P4ScJnikgoi2QS0xqsGmOzjXaGLTRaOtIZiEMSloCD0JfEoWKqOBTKAYUKxGdouEDy1/leNZgRzbIW8q34iUSY8FTPqVSmVKhRDEsYOYX6P7dR7BLy9DXj3zdG0kLw8R5SEeHWBUhw4CgFFAZDCkNFFC+AiQI6MZt6kuz1Jfn6HaaWCAslin0DyClJG42SdoxSTcmjzOyRGPyDKNzrM4xWmOtwVpHslibrz43Vjz0hJAopVCeh1QBiACpPISSSOURBIIgEhQiSViQlEoe5bLCp4NJ6uRJmzw3vfNo0BZyGaFVhJYBufXodnOMkRhjMVmGUgIlJNYoyBU297F5ALl0P1ENWIFYEYdbgaN/pbtGgBIaT2k8qfGlxvcNngd+YJHK8JGHj1GLDcbiSFKRUrIdinSJRE6kcgrKEPmC2a6gJQp0bECXgByFEhaMQSBRQlH0fXQOxjrVppYByg/YNFQlxUMLDyt9fC8g8AOMkhxc7NDOLUHkc/XmAaKiD75AiByRNxFZA6EbCDTCE1AcwFaGISjgDtViRe+EGI3QOUJrbCehO7NA49giWSdFElCMhukrDuELgdBmrViwQoAU5DqmGy+QZx2k71EqD1IoVVAeSN8iQ4UMPWToIUKFzBbwOocRgUBN7EZuvoqZXPLgVJMoU9w4PEhRK/K2Je8YMCCsuz5hJLC6QR6npHHuvp+pwWQ5Ns9cvyVLsVkGJkGIFCUyPJnhqazX1iiZ4ThqSXu+wchdjxB1Ygg9su3b6OiQVgIdq+iGEamSGGERgDAZCIkVCjwPq3OkEUgk0rpSG9yL9isg3ERckLbo2/EYZkuFqUqRGXxesmuc/lKJwA+JggLj5U1srW6l4BWw1qI7Ce2lLq35Ou0HPktbDxKXLwdjkMISBpbQt4S+cUXl5KbJ8eXDnKjP0IkbkGmyRkxcT1mqayIVUSyMoMQQQjgiV4oMJbusPMeshSzrkiRNrNF4Xkgh6if0i2hryEyJyfIom8ptqmGCkC4cVAR+LyzUKZ9nawvMzB3j8mtu59DRiLHNEVsu61vzm1sJCX0GgvFkrB8bXYg+2IuFIFz+h7/HPPZZBt7xBsR1zy18/BLWoFtt2l/9Csn+p1GDA1TuuANfLJwVSTjXjPnkQ8eIM83rrxnnXZ99OdpqgrzAzsW9lNMBfvl7fo5Wtp8vf+NTDAT9vPSG17H5ymsIi6Xn90Av4bxhrWXf11luMk4AACAASURBVL5M7dgMV738DvrHL571Txp3mX7sm8wdOoBUiuroGH0jo1RHxij19Z+3avFsUJ/v8pkPPMzybIf+sSJv/Km99I1cXHuSb0XoPOfJu760KqYq9Q8wNLmVwc1bKFb7LvrnX1SC8FxxiSD89oPOc+anDnLsqSeI2y0KlSoTl1/FyNbtG2b4rTHoep18fp58fp54dpbu7AnSZpNM5xhrkUKipESFIV4UoqIIFRbQKqKZRrTiiDhTYGxvlj7H5qchEp/hqy2kBCURUq3WqVFMHfdJUkEYCXZf20dppEJQjk5VDESKIPIo94co//xustZabByjm01Mu403PIyqnP/sgbUWYw2mN5AWQiCFRHDmUD1jDXEe0827xDqmm3Xp5t3V5U7eoZt1WegukJmMQAXsqO5gR98OtlS34MsXWTiPtRAvQ3sROgs9QrBHCubp2npB0RF7KgDp0xttuo7dKcsK0g4kTUcyrtTrt/el32TjF1DAK37JkYhRBaIBl9ij0A//8EOwdMgRkEK6TuW7PuRIte6Syz7cXXKKnUf/wZFs5TF4y+/D1pfC8YdcONjUV52pfH6yv5hwqsKffeDineezQZ7A45+AxQO95CWvvijhny8kQfjb736zUwCu7Q1XBEUSa4mNRWNRQlFCUEKihMQIsEKRRQMYIbFCYoRabdNZxFqNAQwa7Xnooc1oX6IDgQ4kOpTYUIGvEEph8DDGw6YCm4LVTo1ojSPb9uXHack2ucwwQhMIwVW6D5IYkhSRJAijMQK272/T19IgS6RBH2kUcuSyQQQpQjTxbJPANIiymCAxSAMWS64tmbarvLovDaEyBCrHl478QIgVPSRCgBIK4QWIsAJ+FRFUEV4FvDLCU/SkloAh1zFZHqPzhCxNybOMPM/I0wyd5ujMYnOBsQpjJdZ6GOvByhVauUzCOHKrVyQaQYYkR5IibYYUGikChCwgVBlkH1ZVsaIInt8jK3y8yCB8CwqsBCusq6XFSECCkXZtOZXkiUTHrra5SwpltMEYy3KrBbaLRxefjEBkDJc8JO7+rrUrCMFcOye2HkYGCHxCL2TrUB+5VqS5IDWCxMBy19Bj7pAmQ9oEqROUSfFI8U2OsimgyXWGsbk7ZyLAUyFXToygVICVCqM8jPQxUhKnGSdqDVSeEEjJQLGE55VAhGAFVhuMScjyDmnWIUnbaGOQKqQYFamWQvyCgNBCBCayUAAbudu4ySVkEjKByQVZKyZeqpPHKdgCWXod1o6jVEqpeAQvb0HawVrt7t1+5O6vPWhj6aQaay3F0FDwU5ToIkQXa9rkWYcky09R3FohsMpH+CHCD5FeEelHSK8AKkRridYC2/ueWcBKAcrS0inD9z9MzW+QKvCMpJxXEV6A8T2ED17Bw68EjD30BNX5WcIsI5eCuFrk+O2Xk+cCm1lsahBagJbMisux6+871tJff9p9va1G2gyUQQUWITVBbRm0IIvKJJvGEX4ZKSL3+8sSlG4TlCr4viOZtfXQVpEbRW4EmclJbU6ORihNIfSoFArEcUKSWYwo0mKATaPjDA+FFMuSQlVRrHoEJR+rFLnVZDYnsxmpzUl1Sn12jtr0DGncRRZD/IkBtg9cjT87TLueUSlZJsctocywaepCQlPXfnL/45DnBN7ltFOPPUM1PHmaTqAUCM9fYe1Xz9fKhK67YGv/V3rJbRRvueXZbv3PihcDQajrdWq//18ojaUU3/Pvnz9v4u8ApFNTtL70JXSjSXT11ZR2VpBT/wL92+Dadz0jSdhOcj718DGO12M+u/zvONE5gsEgrGCyfhkvla/hjdd8N91NMzzw6L+wMx5hqDDE2K49TF5x1SmWE5fw3HCm6I8LhSOPPszRJx5lx/U3s2nP5Rd028+ETqPO8f1PUp+bJW41AVB+QHVkdANh2FhMzorMs9ZSe+xR/sefPEYjG6LiL/Fd776KwsQWdG56xWJyw1f+/inay71xkYD+sSLv/dULk0n8YuBCRjKdLYzWPHnXl1iePcGOG27qiaymaS66CI1CtY+hya0Mbd5Csa//ouzfJYLwEi4K8jTlxIGnOL5/H1kSU+ofZGLHLvoHhqDn32HabfKFRfKFeXSttqaYkwJvcBA1PIw3PIw3MoLq69swyxu3MmrHW9SOd1az8JYHIgY2lQiLHlIKpBKIXi2lawsBzfkWn/vTJ6jPx/SNRLzi+/fgBT7dVkanuTGE6OBD8+TpiioFBsaK/MC6G5m1lkQnJDohzmNSnWJX/uxqq7cyp7y+8js7Zf2T1tVWk+mM3OTk1oUj5iZfDU1caWur0UZjVwaKVm8gBTcMFk7CCkm4njCUQmKtJc06WJO7cFqTr3pQCasJURSEJEIy6JfYWd7CRGkTyo9AhY5c84Je23fG9ip05NrFhLWOpFsl1Grr2ktO/beCoORUeaWRdQq9Iff6+SKL18jCv3oHLB9mNWy4PAq3vx+yzobBB+AIwMc+4sjL4iBc872OOJRqY/hjYcCV4qCrT4c8cUlKPvR6TmHI+7a4DJpbX+KSiIxeeeEydp4tjIGDX4Tp+5yi8aq3uYH7BcQLSRD+zve/D8wSq9ddlLlst8JkKQVPMxDm9HspW2v3gBEbRKmJV8a3aY+csWsRrisRxcqifEumBP/9pV8ktwm5TchsQm5jcpOsvtY7D4BAWIE0EqUVgVF4WnHv/IfxdYBngp4KSXHH0FvwjMWzBqE1Mm5hOnWirz1ELg3CpHh5F0/HtF++DUTu7jXWgOypxITFZjkmc0o5GfmIolNdSy/EEiDwAB+ED7JAHg6Qh4Pk4RA2rOIFHkoKvF5RSqKEQBtDpi2ZNr3y7H0XqzVCd/F1Fy/v4OsuKmkjuxkkCUJLrPax1ndKQKOwWmJ7ar/eLRrRuwdiHWkY+h2KXoswSAj8DM9Psb4iLwyigypGhWgZYlSAVSFGhRgZoFXk2iogt4pOuw7xMkHewM+bRLpFxWREOsXPNQ9MNenEEqxHQoQNB3j9LVdDNIBQkdMhGkO3scj87DHufXw/naRLIVDcsH2ActFHKYFU0j0fleCjD81Q62bolVtT6HHzjkE6SU47M7S0opFBPTEst506uZB3qeYNQpMhrUGqgCAsUimUGa0UGS0o7ts3S6cbI4RlVC1zWbHOd+2IqKUwRx8LukDTBCQGCMuI6hA7r7mSl+zdTf9ABc5S2bXh+lpLbeYoH/2tj9NZroMsIVU/1b4C73jl06jCIPnoS8jCUfJ2TNaJybquCDT4modnl1noxmwf8BhWXZYX50mTBKEkpcEByqPDhKUqKojw/AKeHyK0dbYnWUaepegsQ+cZOs9XyaY80+SpK1mSc3S+Ta2RIPZ9Hi2M+11bCHN46XteiXfTK8nLknbWpp21SaaPsPnX/5LCsRrJ5BCL/+lHqWzaRh8FqjakaiNUprFJwt/9/lO0/X6QFnROMV/gZS8RtGeXaC+2WFpsE3czZO7U8HZ9DyGA5etGEEGGCgwDyQxDfeP0bbkN3wuwQpDajPl4kdnOHMtxE5NLirZIlT4KpoBNLVmqKfUPM3HZlSwX+nhgvskPvWY3/aVzsxcxRjM/dYijTzxG0mk5JTCCVj2lsRBjLfQNF+kbdZY0QgiEkHQay4xsu4rabB+Tuyps2hz2/OYSV8fxqr+czR3p7RTI6xSCovfauveCyUn8yclzOobT4cVAELa/fhedj36AwXd8N+r295339i5hI2ya0r73ProPPYQsFChdOUqYfBMxsP2MJGGuDZ9/fJZ7j+7nK43/i/nkKNur27hjyx189P5Pc2P7Fbx555uZHztIy5/j9vxKGkePIZWif3yiR/SMUqj2nXoPtdb5VHdrrv+1rl/v6hXP2XztNa/g+sXFQdcHfR7UZmm3Q3NxkfbyEgMTE1QGL4w37LMRf0Zr4laTv/mVX3QKrh5JNDi5mR/8nT+6IPsAMH9kiv333MXojt3suunWZ33WWePsPXRqes+Stbo+3+Hujx+kvZxQHSnwpvfvpX/0VL/xkxV8r/2hPRhdZ+n4CZZPnKC93EDnBlAcerBFmlYQqh8oEBUU2y8PiRttknZC0jHEqSTJQsxZpq9wky5trE0QIgIR0TdaZnBTiaGJEoO90j9WpL2cnpGgTHLNUjvjof1P8eDfPEm5M0CruMTo92xnaMjdn91Yem2eByBv1aFZo6AsoTCE0tWBMHhobJ71nt85xmi2772RTbufH/LWGM2+r32FpeMz7Lr5JRsyWSedDrWZaRZnpmnMu8ixqFxZJQtLA4MX3ZbpEkH4HQ5rLabRIF+soWuL6GZzdWwLbPBhWt+JSpKYuWNHmTt2FJ0mVIICI6UKReRpv7QiCvGGR/CGhxwZODyMGhhAnCapSLeVsnS8Te1Ym07DzT6UByMGN5UcMVg4u5vT3/za3Swd75xxHSHALynSlj7pDcvYzy+dQgg+X/Ck54pwta/8tbb0UVIhhXRhRuLMBVhTE1qLydqYuIGN69ik4dpJA9I2kVAUhCISHgXpURDe6rJcua7KqXDQ2dkdjPScZ58fubBcr1efvKz8Dd5rq2XVOL/XkVkJD34mEnADsTbgTPSLw44MvMjZN1fxTIlHjIGsDUnLhTSnTVcnLUeuFgbW9j2sPveO2R/e5hKZWKcuojgC21/mvKWax906YR9suQVGLofHPwWNmecvHPnYQy55SWHAdZxPSl5yPnghCcLf/Ok/xqt9FUwdRIQubOVtP/EONu2+nMqQ6/AaY+EDtyEW9yOswQpJPrCbufd9GWMseZ73QpA72LTNlk+/l6B5BIHFIkn6d3Hw+76IlM4/UIqV2hUhXIIGXwl8T+JL11ZyTT38to+/jUP1QxgMEsmOvh18/G0fP+0xHXjjm+hOTaGxaKXwtm5l8o8/4JKo9DpXeZatkiTWWvrHNjE4uXlV3WCMJdWGONMkuavjzBF9ubFoY8i1JTd2w7JeXbYoKdwxKel8CJUgUBKvd6zr26GnCH1J6EkCdfpn0jPBkTuGLNakcU4Wa3RuCIsehUqAFyoSbeimOXG7QdaYJW/Oo5sL0J6HrIXUCUInq5MBK90sy8bQxdBTFAJFwVdEgUdQrCCifqcujvo4noT80mcO88iiYnRkhD/9t7ewdejMiYestU6p3yOsjNYbfHxnFlr86ice4VitzfhQlf/6fTezbbyfICqgTiLpXvu7X+LAfAtjQVjLRJjzik2C+twJsnqNNMuJNSzJMnWvStMrU9SOTOzLG9yxWVJOF6jQoFr2KY1MUNh9K9VdNzE20HduiTPOgD/8yS9gsnmMngXTBgy3fJfCH91DeXiEyuAQ5cFhygODeOt8fJNOm7nDU9x17zeZOT7HUDnipmt3M759B4OTm1He+e+fMZYvPDnHozN1btjazwO/8sMu2rwHYS2/8FMvdx6OV7/jOd3zFx45yKd/737anvMgfNPP3czwdWsJDZJc86GvHmLY99jxsz9Ergp4WZswreORs+2he5luTnNk3yeZnvsm8cRehF9krDSGL32Oto5irWUgGmB3/2729O+hP+rfsA/WWIR0B/bZR09wpNbmf3n5zuc8iDFGM394im6jvkq6pknO/JEGzYUOXqgY3lKiVA16vylBmkySpZK9r9rifEdfRHihCUJrLUu//xvI9hH63/8bl9SDFxHZ3Bytf72TfG6OoE9QHl5ETeyGna+E8vhpf+PWWu49VONrBxYZKgdcM9nHFeMVHlq4l1/+wn9i8/yVvGHkLdT7TrDt6mFeOfJSjj31JPXZ4ySdNgB+GFHtK1EtCPrCnIJtcHRpPz9z7HNM5U22exX+YPSVbPHKp93vuk54LFuiazKiXr8/UgGFaICoOExYGqFQGiOsjKNKI67f/hx+39YY2vVlmovzNBcXaC4skHRa69YQjO/aw9Zr9m64X598vrD2GUNl026H+twsH/vNX6O5uGb74kcRw1u3EzcbdBsN4nbrtP8PcP13v4mtV1/H5quuec5egfX5Lp/8va9QO3o/pYFB3vkf/mcGxtbOv84M7XrC7FSDu/7xadr1hELZZ/fNY0RFN9a1xk3ApLEmi3MOPnSAbu2jWLOEkANE/W9nbMcWpCeRUqz6B89N1dG5ddfI2t64fu16WZtgTQOrG5h8Fp0+BDYGUcALb6MoCoRCEpqMUCaEKicILE/qWzeo8bGGlw4+hki65J0m3W6dbtxgn9pEag70thnh+3sY2raNuOPRbSgsEUIUEKqAkD7GWESvpys88MY8siQnzww2d17TKlZO2NJbL/MS+m/ZgjcSogZCJxjqWX0lR/eTzBxAa0OaG3Ir3DhTqV7tEYYBxUJEYCT1uw+TtWqUx67n1T/1XUxMVggu0nPEGM1Td99FbWaanTfeyviuPc+4bhp3qc0cpTYzTX1uFmsN5cEhrnv16y/IvlwiCL+DYK1dU5ThiCElFKKbYJeW0YuL5LUa+eIierHm/HN6kMUCq1PbPWit6SYx7bhDO47pxF0ynSOUYnBwmLGJzZQHhpCFCBEVenWELBScGrBYwBacSmhFOeeUJ2vquizNWZhus3ysS9zMEThScGBTkYHxIlExcDPFrCnfcps7tV2vXlHYZca1P/8fj54korJU39giDbokfhtbyCF02YHt3+2A5cD5RAmLGtBM/khC5EWEKiRSEaHXq1VIoIJV8m1Fhbc+fPeU9vplITa8thJkJ4RYJQXPe2bA6F6Ci1447UrCi25tYyis8nqqtCFXe4U1FaC3ogpcX4drnRtjnD9fnjjvPp2e1E5dMo6s68Jes+5aybtOcbdeQnUukGqNAFyvrisMnh+x9u2CZyIorXVJQ47cDUe+7ur5J9b+T0gYvsxlR77YWDoMj30MzpC85LnghSQIH3/wy3z6Dz9HkJdJCwFv+OEbuPaWV5264tlkrn4u654lppvT/MwXfoapxhTbq9v5g1f/AVsqp8+Qer7ZVL9jYW3vPhj3EhKdXCe9JCl9a+X5VvQ+C44sdviRv7iPg/Ntdo6UNhCUWZpy5PAMh6eOcPTwNF946CDdzN3Pc+kj+4b4hbffzuTWSTYNFAkWnoDjDzqrBOW773Jlk1NXl8fOffImacLyNNSn+ZsPWZaXvZ6/o6Y6aHjND2+nVVukVVuk22ys/luhUqU8OETS6azOzJcHh6iFwzzYDBkZqLrMoheAvNTG8rnHTrDvRJPbdgxy+64h/vznfozaieOr3azB8U380C/9OOz/vEtOtee1FyXJ1ANHlvjSvnne8Ds/jz0y5Z7fUhLs2MGuz3zaTbTd80HM+LXMbbmRI40jHGkeIdEJu/p3sbt/N0PR0Fn1Tf7ia1P0F33eev35K+9Oh/p8l8OPLhC3MgYnymy9apBuK2Xf3SfYes0Q4zsuvm/TueKFJgiz6cMs//4vU3n5rURv+dnz2tYlPDusMcSPPEL77nugeYLiQB2/v4AolhDD25HjexAju0+JBnl6rsW9h2rMNmKkEGwfLjLU1+J3H/5lksOSO4I3Uqh6vOJ/uoFrhsaxjeMkC4epHz1EY/YojaUmSapBCPJCgQ92v8FM0KQdaVLfUAiKvHv3WxkpjjBSGKGoBlnophxYPsa+pX08Nv9FsrRJqEpcGVxDWSqUTVA2wbPJqoVKgCSQEUPFTUz07WTb6BVsHr+MUv8oeSpI49yVbk632aW1tEjSWSJpL5G0l7HWIKUgLBapjAzTNzRMuVAgEpaZp55gduoASkg2T2yhv1Cm223QaS3RadepLc7yzX0HiJOMwPcYG+kHoUgzTZykdLsxaZqe7rIAMDo6TqAkykis9rA64OjyDNa2163lIcWaTdNAMWRTtcxYpUwUBNx1YJpmHFMtFnntbTcxMDyCLFWQxSqmUCYLCzS7Of/0l/fSrt3pSDI5QGX8Xey941qSTka3lZHFGmssR5+coru0jvQbeAelwVGSdpuk3QDdxtqOs7+Iv+62twJRIhp8C9KrIv0KyvfwQkVz5hhZ+xOr2/RLb2XPia9iki45HTLRJZUpiadZLElg43gs0gZpLJ4xRGlOFKcU04wjW19Hx+wH2wJRJGCC0eajJIFHphRGCawQtLR29ho9KG24/o1vwS8UQSg6rZTlWpfGckJzJkGIIlININQ4CMmJykFymZHLtFdnXDF/2+qYGRynsLIslWBwskT/mMUkT2N0h9mpgG5njP6xPl7/k9dBwWO5mbDcTGi0MlrtlHY7Y+FfpjHtFJs8jiWBob3Y1++mGCiqBZ++XqlGrq5EHpGvCHuk7GnvAT0uBtgQsWeNYf+9X2Nh+vA5h5tnSUzt2AxG5xdM6XiJILzAcIbwGiGEI9+exdvtZKyEg2qrV0NGjTVkJiPVKbGOSfJkNax1RcmW6IRYO0VbprNVAnA1tNSuBY/INKc4U6N0tEaw3EHFmTPBR2KjEN1fxvRXsAN92IEqYqAPfB/TTdD1Dnm9Rd5ok7e6q0SbV4zw+8t4fWWCwSo28jaEvOZ2YwhsbvJnVN5ZC7Q9mItgKQQjoJzBYAIDKSJ4juTRyvZPQ/pt/tGUolek5Jco+SWKfpGiV8TUFXf+Pwe+9cxUjXEd+/a8K6s+e7U18k0IR5oVB3tE4CAUe6RgWL0og5GzgrVukJx13KBZp6xmGBXSldWso2Lj6170HUcCnmmwfl74tUGnzFyBUPB/1s5/u2eDTg0e/Yirb3ivS65ynnghCUKA5uICxb5+1GnU0ZdwCd+uOHB0gX/353eyvyGYGBviT3/w1lPvT9Y6BfOxB12G92SdeiOqOqJwhTAsjzo19Yr6IV5eJQRZnnbPPQDlUxfb+MznhlletPSPn/r8ztKEdq1Gq7ZIc8mRhp7vM7x1O8Nbt69mDZxaaPNPjx7Hk4I3XTfBRP9z7wPk2vCZbx7n4Hybl+8Z5ubtTiX9jCFvT38Bpu+FXa+Crbc958890/78+dem6F+e54Y//o1TCf/HPwkL++C2n3B+vM8Rcab5ozsP8NJdQ9y2c+gCHsFGGG04fqDO8aeXQQj8wJHr175yEqlefH2DF5ogbP7tB0ge+CpD//6/IYa2nde2LuHsoZtNWl/6MunT+9w9rNsrOkFIiShWEH2jyMFNiIEJZKmCCCMaKKaaOUeWWuS6g++1+Vf7KR5vLHJr86XsVBXeslOyqarR1iMPR8ijUQ5ruGf+EI9PP87C3AyFriDIBNI631krBEYpciXRUuAZSZh7RJmkmCqiTPTcNTSekIwVtuBLhRIKKSWQkuoGU8kBuiQIr8iQGnL+vLJIIIuEQZnQd2qu2LR4aukxurpDUZa4angvlXCQQEV4BMgcSFJ0ElPvzHPw2J1YkyCkT19lEovGShCBjzYpedYlqddOteoRAhl4KE/17KZcYq90ISX11sY4ni3RF7yZbmEI00ukaE0XnT5FHn8NbLenoHsJMtgJtoXJpjH5EUx+HJd97GQopKqC1b0kajmQcYrNz7r1EQFC+KzYrVi9AKxPWNkb69j8GbbxzJAyQqoiedbs7cfa5/rWkomNY+tSVKTdbW8cC1rL+OgA3TghSXPSLHfRL2eAEKB8iR8qvEDSXIhP2ebKshVgSgJdEOSRwF/YiR8fXT3/afUW6rcfwvcCPOHjCx9P+Ni7duLVvuisfOQAevBVlG7tQr0AywFmfhnTWgQbItQoeeeLawRp+a1IdXp7JqOXyVofx5olEBEq3Mu+3VOIQIESWCWwniSTOYfFAdqyRcWW2CuvpWwDlM1RViNNhmc10mpkrvGsRWJx5KtFWotezjBtTdDv4Vc8ein9nCd3b6wrpXTqWOmS8AmpQEp0N2f+wVmyTsrQ5JYL4pX5HUsQnq3xaCfrMN+dZ6G7QJzHq0RdatJVdVqq09Va21NvEqeEd64L/7TYVQLNWLPqH3c2EAhCLyRUayXyInzpb/gsIQRSG7zpOdTUDHJmFqEtoq+CGRsi7yuT9Rdd7UuSbpuk2SRttcnaHdJWh7zVxuTu2ISSqGoRVS0iK0VUtYAIepJn5zCFJz1U7+GxEha70l6p158TgcDklu6cpTWTk7Us0hfo/jb/7+z/zVT2NBOlCX7xll9ktDi6qoJcIT7XJ95QQuFLfzXs1lfr2tKnW8v51w8+TX2u+61F+q2HtY5AS5obk2J0l3vqwJpTC4K78Ub9PY+94V5Y7YgjBp/B++QSvnVwx2/fydRCe9UBYLQa8lvv2stoNWS0EjFQXAsPPCcycUM48vOoIFxBFsPM/S7hygUgfV9ogvASLuESzhJpG1qzLjv7St1ZXBv8eaF7hsV199wDZ03RtwX6t7q6PHZBJ4tq7ZRPPDRDM855zZVjXDVx7qFlaW741MPHOFLr8KorRtm7pf/Z/8laePzjMPckXP025xF7gfHYsTr//Ngsb7puE3vG1pGArTm4/0POo3bXHef1GUcWO3zkgaO848ZJtg1d/EyrcTvj8KOL1Oc67LxhhOHN55/k7WLghSYIs/v/ifz4QQpvfv95becSzh3WWnSthul0sHHsfDGX5zCLh7GLR7HLxzFxis01RhaxxsN2W5B1sUaT5Jp2qulkhv2iyaNeg0jdjFQDdEWOF0T0FYeZz5do2Da5hELUT7U4wdH0AHG+iNQJfmYYiAMm9ATEC6i0izIGTyqEJzie10gCyDyBkQorFQgPKzyQHoqAslchzTKX7d2ANCmRyRkSAbq7RK5bGJ1gbY4gpUuCl1WQVmFFTh7UKakCCEkufLLAI/MUuecRzSZ4emMM20b5giAo9pF2lk86w4JtV30PCOGsUDCOgPI82vuKdJLPb1DRBb5CqAQtGhgzi7HL0Cxh/c1IbxSTzyOyoySbu1hVAX8MYYaQHQ9Vr5HX//GUayz87T1BQ441zotXqADTOXrKuuVqCatTMM7D2RropqfyAdUSeJ7E8wSeL/ECDy9QTB+OybI14lAFHkPbi2SZcZ63qUEnmqyenUTQgSwPIVWEkBFSRUgZoJB0JBY8pQAAIABJREFUFh/F6GT15HsyYHhgdy9Bm0EIjTEpqY6ZX144ZV93baoglcVagxEGIywzM4nziu5N8kmliEbHMJl1Pr5ZSp520dnJiRXd9VfSQ0kfT/lIqZBC0eouucRjPUjhMVQYQxtNqrtoa/Ckj1JlluMFsOtsxkSRYmGb85Om57eJBnLaydJJqswIL6pi/T6s8HpJAk+9Rj6ScbHuOWeB9akK1/mJGGtJ9BKp7hB6VXxvfd/CcRzCWmyPTFx5jdViWGztxxinjr1QXpnfsQThB3/ux2mcmEHg+Nv+TZt572/9LvPdeeY6cyx0F5jrzNHO1qTFvvQJVIAnPQIVuGUZrLZ95ZY96W1IEHFyWO/69wRilUhbrdcRaetJtkAFqyGtoQoJZHDmzLN5TnrkCN19TxIfPIRJM0ShgL9rJwsjIf/7I/+V5cYCW9Q4P7r7fVRNkbjVxJq1WYSm7vLJqc8xnzcoDw7x86/6D+yavBzPVyjfSWjPN+y120yZO9xk4WgTnRmK1YDR7VWGJsu889PvOGtPrDPCWueNl7VdKGvaceGs6xNvbCgnvQYbVWyrCjbVU7GtvKY21lI5rz3Rq6VcWxZinSGwXuerd5JRsMkhba0jA5tu2ZxERgvpZvhXiMDSyJrH3iUi8NsGJ+oxdx9cXC1Ti2f20/SVYLgcMloJeXquRTtdkbbDrpEyn/+FV5z+Hy9CCOsLiUsE4SVcwrcwdObU8KvE4Zx73vVvgb6t7jl3kVXvcab59CPHma51uHn7ADdsHaAUqLPqA8WZ5hMPzXC8HvO6q8bPjWDUOTz8Yaey3PseR4JeQBhj+et7DmMt/JuXbFsLjXrkH5wy8yU/ed4+vfceqnHX0wv85Ct3EfnPT8i8tZa0mxMWX7z9nxeaILyEFzGMdh7QS1OuP5Z1sEEfRpWxooQREYaIJJVMH1/i3kP38bWZv6Y/3kyQe/h5hp9nRLJAQVYoq36KXpHQkxhP80hyD+28znBWYHt1B6WoSN/4VrbsuoGByd3YviqZkvyv/+P9LCyfwM8gSj1G5SCv3/Y6mlmLVtqkZTosyzbfqD9C4mtyBcVEUun4FG2IsgKrLCawGKWxImPPNytEqb8hLPRsYbFs2zNKGDkvuizTBKHH8cNLxN01ZVyxr8rt7/5+wtIAhWI/xcoAflTFGMHf/vrdp/jlvf1/u5y5Q0/SWDiB8AUDO7chuoJ7PzRDxx+kmNW4+YcnoCJYPnQEE6eU+waYvOwqRrbt4Pf+zQ87pVlvul7IAd75f/wiM089SRrHFIdGGd5zJdHACJ/8lZ+jPXd8dd3q6Cbe959/nUB3EWlr1Y/8T3/3z1mu1VfX6+8r8YPvvYEsT8kwZLZXMCzXY+76/BTtekKpL+Dm12yhUA0cKWV7lJIQ3PX3T9Fu5qsEXakacPuPvRorA1IT0Ep8mqlHM1E0F9u0v/R5aNWxxX7i69+MCKuo3CByg68hMOBb6D76d5h4mRUaV4b9FHZ+r/MQtAK5wgmmdTrH/hmT1vGCPjZvfz2VqI9AgkITyJxApEjd4V8eOnW8P1IdJMlTLOBJBQKWmvVz/h6d3XdtIxltgR179nDF9XvZfdkV5Jml20356S/9R7zcR1oPIwAl+e07fsvZcPmBy6ugFEK52kiFts5u5Ni+h1iaOcTQtssZ3n6V44tML7GK6SUrNc6eQFoDxiVDay0cY+HIEyxOP8n8kSc27LeQkl/48CfP69if6dn0LRkDdTbqmFwbZhfrq+QgOAln/fhR/uzuP0ZUXAbAvrCPifIEI4URRoujDBeGCdSZs66l09NM/8iPkx4+TLBjx3l5MlljiDttZ5jabDp/Bq3pamcubrXGGIPRGtN7zWiD0Tn58jLp/BzZwgI2yxGehxocxBsaQlZKiOU5Pvz1D1NJWlSIgDp/cvyPECrEV1UCv0qo+gjFIPW4RW63UwJsTfBnn/oM77lizUhVSIHyZK8I5Gp7LTvi6nu9jIkr7+ncMn+kSXOxi5CCwYkSo9uqlAfC1Q73VGMK0/M/MBimGlPrTpLtkX3rkjqslKzjSMBsXdFnIcleIfqkt66otc+z64k8s7Zs7amE3YWE9NxgKKy4cMuw4sKAwwqEZVf7pW+J8NqLFhL7bYaV83RgvsVIJeSWbYM8eqy+SghWI4/bdg7RSnIW2ynWOtJvy2CR33rXXuabCXPNmLlmwlzD1SvkIICxcHC+/Uwf78jA51MxeAmXcAmX8ExQPlQnXHmBEPmKt98wyZeemuP+qSXun1qiEChGyiEjlZDhXj1YClDr/Ic6ac7HHpxhsZXyxmtPUumdDdT/396dR8l11mcef363tu7qfZO61dplyZIl2ZIsI7ywmDEeZ2xsw4CBQGzOZEJIgITgjIkzHEwIECDEwMAcCA4ZAhP2xWxh87A5GGJLRrJlWbJk7VJLam29d3VV3Xf+uFXV3VJr7VJX36rv55w6detW6db71m3dt/upd4kGizY9+cVg6oXVfxAEokXieabrFrXqe5sOaktXr1Z0NgTDtY/tkBa+pCiLeB3qHVZTMjZl4aAU9KSYzuEgcFZeJPgyoHGutODFkoKw4tT/QVWSGiSt0Mv0iS98Sb4b7Z3mOelPl7xVbqhfmZGUBkcyGhlOKzKc1tLUOlnWybU0as5la3TF5dervub0heE++NpPjZmbuEP33fgJzYi0aKi/V8N9fYX77scOq+/ECck5ZSNSsrVZH3jFx9Q4s12J5Phewx993W2nBS9v/ODHlJZpeCSj4eEhpUaGlU4N6Sef/6iSAyZPJl9Og3VOi1/9NiWqqpVIVCvV16ujO7aqZs4x7d7wuAZOHj/ryEBJGqo+ruqhZjmZXPa4Ut5z2r3xsGKJKi1cvVrti5YUFkJZfsPp/95fmdWx/fvU9dxW7Xzyce3dvEn17S9S76FfyfknJa9OyabLdWDLU2pqn6XOZStU39pW+Peve/f7ThvFmGg4vaz/9b0rTntdZGa7Is6pys/k5nZPS35Gs7NprbgpHfyURGKjf8MWtmOS52nOqvMbQVnwB/fI952G0lkNjGQ0mMrdj2Q1kMpoIJVVfyqt6NIZ6v3+P8r1dCvSOEPtt75F9U1tSnhecIuY4vIU86SYbpFnUiIZVaImpqrcLRofv3jck+/coBMHg8WwzExNnbN19z98Wr3dR7T3mafU231Y8eqkNv74++rtPlJ4XbKhUVfedIta587XvCtXKxKNyflZOd/XF+57h3rGhLMNMzt094c/Hgztt2AIr3kmM0//9Bdv1smuLnm5V3sRT7u3b9fu7dvV3DlHS154vRauuUY2t0ULf5FS/UBUvTUZbb+xSh3XX3Xmz1TBF1i7N26QSx3S8uvXau7Kq8bV/dSRrjf997fqRNdB7Xl6o/Zu3hQs1CWpedZsJWpqg0WJ8qttz7o08/xKIe1B+PIHf6kd3f2FRXkakjHdePkMdfel1N3fr+6hQxpOHdSte9dr5kAwJC8//aZJisTjuvLO23XdbXepOjEaWJzvxO1bf+8WZXftKfwgWTSq5NVXK9bRoWhHu2IdHYWbn8nq4L33amjPHrn589T87r9WJpnUUF+Phvr7TuvJF4nGFIlG5UWDoblmTi7ryx8YVKanT5m+AWX6hpTtHwpW9fGiijTWK9rUpGhDXTBOXaZgYLKnj+/4lmSSp6QiSkrmaU3TUqVdVqnIsIZjgxqM9ut3/Rs1HB1QOpKS5yKKu7i+deu3lc0GKzpmM76yaV9+ximb8ZVJ+/KzvrIZlwssg/3uDHMUJJIxtc2rU9vsWsWi2dFJ2nMTtt/5i7dp18BB+XLyZFpQ1aqHL7t7NAicKJSLRIOwLFYdTPYeqw5W1Sps10jx3Cq544LA6OQCtiDuH9P7L98T8dTHmdFQ0fPG9DaMjm6f2iMxEi/dnIBFNnYFzHP2YjsPzgUroaazTtmsU9r3R1c5zTpl/GBF1NHVT4PnfRd07Q4WPQseB8PVcyt+5p6/EOd7ioJjj76v5Eb35crwgR9s0eHe1OixJS3rqNf81qTmtyTVWpuQZDo2kNJXn9inYwMjak7G9ao1nWqojuWOP74OX/ztbp0YSBe+FWuqienua+crYpbrDaxgO7fqbbBCbLDqayIWUTziFVaBDW7BZLzxqKeqWETJeETJeHTcH8fTBT0IARRLV8+QDvUM62j/iLr7UjrWn1Im93tOxDM118QLoeEzB3vUM5jWbVfN0oLWSQyvHToRhIReVFpzd/DlYJE45/TVJ/apP5XRm66dp+hTXwreb91bggXKJumhX+3UnOZq3bKCVXLHogchiunOh+8846irtJ/WcGa4cBvKDslkmt8wXzFv8kH6vr59+rOfvk37T+xVZ/NcffKmT51xkbOH3vFHheAlGMXXoT/6+EMTvvbZnU/q63/3HlX3OQ3VmV5z//u0bOGaca/JZjI6sPUZHdi2Rc73C38zR2Kx4BaNjdmO6uCxo/r1wzuUGBhROnJcy1+yUKuvvUEz5i+6oHminXPq7T6ig9u36tCOPXruicMaHsiqKmm65rartWTdatU2X7o5V8vduaaD6zlySHufeUrdu3dp228e1VBvj6rq6rXyxpdr+UtvUvOs0+cvP98p5iZ67R3/4z3KpIb12Ne/pCO7n1ff0e4glPQ8+b4/ZlTqmX+epeDnZs/TG3Vw2xbNWrxU865ac9pIhM/f+yc6dmD/afNq1jQ2ae7KVZq3cpXmrrhKdS2tF1Sn81VWQ4wX3f9vqh88oSv6dsosq0hkRDVVacWjacUiWcV9p4YjXfJGRnSypkM1Q8cV84c14iV1oGqxWkb2qyFzTLHaJq17+W1acPkSxauq9M5fvVP7BvcrlpYSaVOTq9HK2qXKDg8rOzQiP5WVUlmt2NCrbCSibC5kcpIidfVyIyPyR1KnzZmQ7+4r82TJatWtW614PK54IqF4PKZYPKZstkpPb27RwGBCyeqMlrXvlaWGNDLolB3OjIaI+YlYq2OKJSOK1SVkuWW9zVxwL1d4/L97NqnbH9RwZFgjsQG1JkyfnX29quIZRSKj5/7Og9/XrkyvfAVh6oJovR6eddtoz7px305Egm8o8pOHFz4Fyc+6YLSsL2Wzku+b5PtKJoZk2eFgZdsJVq7dl+nX24/8QrszfZofa9An592pObWdUrw2CPzitcEvyfnteG1RfplFcTnnCqHd8vf8WNkx1xfPpJ/8xUuUzvrKZPNh3+htJOPGP846pTNjXzP5a5WZglBMKgRlwcrYE4d+xbg85kM4y71voQy5fe/5zuZx72MmPXjXVfJyAV7EC8o39rHG1qNwrNG6HOlL6cM/2qqDJ4c0q6Faf/mfL9eMuoSybmxg6pT1g23fD85HKpO7pbMayfoayfhn/AzMpOpYRMlEVDW5wLAmEVFNIqqaeFTJeES1iahqElHFo1PX45WAEMCl4vtOJwZH1N2f0tG+EXX3D6u7L6WBVFbxqKfbr5qlOc1F6Cnf2yVt/L/BYmJXvi74srNI9h0f1Dc27NfN7f1afvTH0pKbgxWUJ6lvOK1/enSXXnp5m1bPnXgy+EpFQIhiOt8OLaV2KQINSRrs7dGxfXuUTaeVyWSUTaeVzaSD+3Ra2Uym8Ng5X9V19epculytc+fJ8ybXu3mor1ddO7Ypm8lo1uKlqmnkWjcVnHPqOXJY+555Sn3HutU2b4Hmr7pasXjikr1nanBATz3yI/lZXzWNjfrxZz4x7vmzDfFNj6S0f8tmdW3fqvbLlmjBqrUTTlPy4OtvH9dRTGa65+8/pZbZcyc9tdv5KKshxgvbalS9c5uuSh2WmZNnnuoVU9RLyCT19D6vbHZE7U2Xa0GiUXtHWpRyWSUjpnVx04CbqUE/ppH+o/r3b39Rv/Hq1ZicoWUjTVqmOuV74EmenOcrompFvVrJC8aVH29JKZH25Lkg/BuJSY3zZgUTjmazcumMXCYrl8kqfTwY2ufJU8RFFElFFDsQ9BIbUUZpLyuzlLanOpVyVZKZBodjemZvp65uflb1LTVKNLco0daoqpktqprZonhtUtFEIhjrbrm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      "text/plain": [
       "<Figure size 1296x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.MVP.show_preds(sharey=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fine-tune 🎻"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are at least 2 options to use the pretrained model weights: \n",
    "\n",
    "1.   Fine-tune\n",
    "2.   Train\n",
    "\n",
    "\n",
    "We'll start by fine-tuning the pretrained model.\n",
    "\n",
    "In this case, we double the base_lr as the training lr will be the base_lr / 2. The only net change of the fine tuning then is just a training of the new head for 10 epochs. The rest of the training will be the same.\n",
    "\n",
    "Before training though, we'll check that when the model is frozen only the last layer is trained: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weights from data/TSBERT/LSST_200.pth successfully transferred!\n",
      "\n",
      "trainable params once manually frozen   :        0\n",
      "trainable params after learn.freeze()   :     3854\n",
      "trainable params learn.unfreeze()       :   457614\n"
     ]
    }
   ],
   "source": [
    "learn = ts_learner(dls010, InceptionTimePlus, pretrained=True, weights_path=f'data/MVP/{dsid}_200.pth', metrics=accuracy)\n",
    "for p in learn.model.parameters():\n",
    "    p.requires_grad=False\n",
    "print(f'{\"trainable params once manually frozen\":40}: {count_parameters(learn.model):8}')\n",
    "learn.freeze()\n",
    "print(f'{\"trainable params after learn.freeze()\":40}: {count_parameters(learn.model):8}')\n",
    "learn.unfreeze()\n",
    "print(f'{\"trainable params learn.unfreeze()\":40}: {count_parameters(learn.model):8}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems to be working well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.601 +/- 0.004\n",
      "weights from data/TSBERT/LSST_200.pth successfully transferred!\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2.632525</td>\n",
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       "      <td>00:00</td>\n",
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       "      <td>0.406661</td>\n",
       "      <td>00:00</td>\n",
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       "      <td>3</td>\n",
       "      <td>2.489961</td>\n",
       "      <td>2.192620</td>\n",
       "      <td>0.405428</td>\n",
       "      <td>00:00</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.397038</td>\n",
       "      <td>1.999449</td>\n",
       "      <td>0.400082</td>\n",
       "      <td>00:00</td>\n",
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       "    <tr>\n",
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       "      <td>0.400493</td>\n",
       "      <td>00:00</td>\n",
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       "      <td>0.414885</td>\n",
       "      <td>00:00</td>\n",
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       "      <td>1.663277</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>00:00</td>\n",
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       "      <td>00:00</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.999781</td>\n",
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       "      <td>00:00</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
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       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.432279</td>\n",
       "      <td>1.507713</td>\n",
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       "      <td>1.394723</td>\n",
       "      <td>1.497789</td>\n",
       "      <td>0.495888</td>\n",
       "      <td>00:00</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.381752</td>\n",
       "      <td>1.488996</td>\n",
       "      <td>0.494655</td>\n",
       "      <td>00:00</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>00:00</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.364290</td>\n",
       "      <td>1.466587</td>\n",
       "      <td>0.502056</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.366068</td>\n",
       "      <td>1.453052</td>\n",
       "      <td>0.505757</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.362425</td>\n",
       "      <td>1.439588</td>\n",
       "      <td>0.511102</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.344054</td>\n",
       "      <td>1.422833</td>\n",
       "      <td>0.526727</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.338439</td>\n",
       "      <td>1.404448</td>\n",
       "      <td>0.539885</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.326871</td>\n",
       "      <td>1.387469</td>\n",
       "      <td>0.547286</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.316357</td>\n",
       "      <td>1.372098</td>\n",
       "      <td>0.553865</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.299873</td>\n",
       "      <td>1.356515</td>\n",
       "      <td>0.553454</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.282781</td>\n",
       "      <td>1.338759</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.272574</td>\n",
       "      <td>1.322370</td>\n",
       "      <td>0.556332</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.258363</td>\n",
       "      <td>1.302713</td>\n",
       "      <td>0.569079</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.244002</td>\n",
       "      <td>1.287691</td>\n",
       "      <td>0.574013</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.227531</td>\n",
       "      <td>1.278773</td>\n",
       "      <td>0.577303</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.208770</td>\n",
       "      <td>1.271010</td>\n",
       "      <td>0.580592</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.188091</td>\n",
       "      <td>1.265602</td>\n",
       "      <td>0.582648</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.169838</td>\n",
       "      <td>1.263533</td>\n",
       "      <td>0.575247</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.154788</td>\n",
       "      <td>1.256896</td>\n",
       "      <td>0.572368</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.134324</td>\n",
       "      <td>1.254300</td>\n",
       "      <td>0.579770</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.117796</td>\n",
       "      <td>1.252475</td>\n",
       "      <td>0.581414</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.101251</td>\n",
       "      <td>1.256413</td>\n",
       "      <td>0.583470</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.087506</td>\n",
       "      <td>1.248259</td>\n",
       "      <td>0.592928</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.076043</td>\n",
       "      <td>1.242493</td>\n",
       "      <td>0.583470</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.060271</td>\n",
       "      <td>1.237767</td>\n",
       "      <td>0.581003</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.046030</td>\n",
       "      <td>1.230913</td>\n",
       "      <td>0.588405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.031314</td>\n",
       "      <td>1.226287</td>\n",
       "      <td>0.597039</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.016448</td>\n",
       "      <td>1.224149</td>\n",
       "      <td>0.596217</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.003537</td>\n",
       "      <td>1.218494</td>\n",
       "      <td>0.598273</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.990356</td>\n",
       "      <td>1.216215</td>\n",
       "      <td>0.604441</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.978654</td>\n",
       "      <td>1.218552</td>\n",
       "      <td>0.603207</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.965965</td>\n",
       "      <td>1.219160</td>\n",
       "      <td>0.602385</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.954571</td>\n",
       "      <td>1.222372</td>\n",
       "      <td>0.594161</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.943869</td>\n",
       "      <td>1.220932</td>\n",
       "      <td>0.599918</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.934301</td>\n",
       "      <td>1.221948</td>\n",
       "      <td>0.601562</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.924389</td>\n",
       "      <td>1.222099</td>\n",
       "      <td>0.600740</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.916539</td>\n",
       "      <td>1.220464</td>\n",
       "      <td>0.601974</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.907470</td>\n",
       "      <td>1.220543</td>\n",
       "      <td>0.599095</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.896671</td>\n",
       "      <td>1.219566</td>\n",
       "      <td>0.601151</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.888102</td>\n",
       "      <td>1.220812</td>\n",
       "      <td>0.600740</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.880152</td>\n",
       "      <td>1.219794</td>\n",
       "      <td>0.601151</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.874156</td>\n",
       "      <td>1.217364</td>\n",
       "      <td>0.604441</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.867738</td>\n",
       "      <td>1.217077</td>\n",
       "      <td>0.605674</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.860430</td>\n",
       "      <td>1.219881</td>\n",
       "      <td>0.604441</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.853081</td>\n",
       "      <td>1.219927</td>\n",
       "      <td>0.603618</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.848616</td>\n",
       "      <td>1.220163</td>\n",
       "      <td>0.603207</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.843981</td>\n",
       "      <td>1.219946</td>\n",
       "      <td>0.602796</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.837639</td>\n",
       "      <td>1.221042</td>\n",
       "      <td>0.601562</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<IPython.core.display.HTML object>"
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.601 +/- 0.004 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# self-supervised: fine-tuning with 10% labels\n",
    "n_epochs = 50\n",
    "freeze_epochs = 10\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls010, InceptionTimePlus, pretrained=True, weights_path=f'data/MVP/{dsid}_200.pth', metrics=accuracy)\n",
    "    learn.fine_tune(n_epochs, base_lr=2e-2, freeze_epochs=freeze_epochs)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.722 +/- 0.003\n",
      "weights from data/TSBERT/LSST_200.pth successfully transferred!\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.379314</td>\n",
       "      <td>2.146272</td>\n",
       "      <td>0.323191</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.085978</td>\n",
       "      <td>1.760187</td>\n",
       "      <td>0.416118</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.811320</td>\n",
       "      <td>1.468608</td>\n",
       "      <td>0.520148</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.575074</td>\n",
       "      <td>1.272919</td>\n",
       "      <td>0.561266</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.384338</td>\n",
       "      <td>1.149786</td>\n",
       "      <td>0.620888</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.236759</td>\n",
       "      <td>1.084872</td>\n",
       "      <td>0.653372</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.137518</td>\n",
       "      <td>1.052009</td>\n",
       "      <td>0.662829</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.062143</td>\n",
       "      <td>0.987593</td>\n",
       "      <td>0.676809</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.003800</td>\n",
       "      <td>0.967438</td>\n",
       "      <td>0.685855</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.971565</td>\n",
       "      <td>0.979494</td>\n",
       "      <td>0.675576</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.862462</td>\n",
       "      <td>0.926791</td>\n",
       "      <td>0.699836</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.846062</td>\n",
       "      <td>0.923621</td>\n",
       "      <td>0.700658</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.846275</td>\n",
       "      <td>0.915242</td>\n",
       "      <td>0.698602</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.836305</td>\n",
       "      <td>0.917706</td>\n",
       "      <td>0.702714</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.830797</td>\n",
       "      <td>0.921918</td>\n",
       "      <td>0.700658</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.831228</td>\n",
       "      <td>0.912100</td>\n",
       "      <td>0.702303</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.820537</td>\n",
       "      <td>0.907763</td>\n",
       "      <td>0.708059</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.811878</td>\n",
       "      <td>0.911042</td>\n",
       "      <td>0.703536</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.805218</td>\n",
       "      <td>0.904801</td>\n",
       "      <td>0.704359</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.793261</td>\n",
       "      <td>0.901012</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.785474</td>\n",
       "      <td>0.901891</td>\n",
       "      <td>0.709293</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.776986</td>\n",
       "      <td>0.896143</td>\n",
       "      <td>0.708059</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.768123</td>\n",
       "      <td>0.883702</td>\n",
       "      <td>0.714638</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.757132</td>\n",
       "      <td>0.899261</td>\n",
       "      <td>0.712171</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.742552</td>\n",
       "      <td>0.900630</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.733242</td>\n",
       "      <td>0.879378</td>\n",
       "      <td>0.723684</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.716872</td>\n",
       "      <td>0.887551</td>\n",
       "      <td>0.717516</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.704935</td>\n",
       "      <td>0.882421</td>\n",
       "      <td>0.715461</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.684582</td>\n",
       "      <td>0.899347</td>\n",
       "      <td>0.710115</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.666077</td>\n",
       "      <td>0.885893</td>\n",
       "      <td>0.712582</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.652506</td>\n",
       "      <td>0.883074</td>\n",
       "      <td>0.718750</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.646331</td>\n",
       "      <td>0.898239</td>\n",
       "      <td>0.705592</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.634330</td>\n",
       "      <td>0.889329</td>\n",
       "      <td>0.720395</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.621489</td>\n",
       "      <td>0.881531</td>\n",
       "      <td>0.723273</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.615062</td>\n",
       "      <td>0.877867</td>\n",
       "      <td>0.724918</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.606734</td>\n",
       "      <td>0.894344</td>\n",
       "      <td>0.715461</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.588674</td>\n",
       "      <td>0.884271</td>\n",
       "      <td>0.724095</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.579864</td>\n",
       "      <td>0.901384</td>\n",
       "      <td>0.711760</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.572145</td>\n",
       "      <td>0.887300</td>\n",
       "      <td>0.721628</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.561631</td>\n",
       "      <td>0.879308</td>\n",
       "      <td>0.723273</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.551189</td>\n",
       "      <td>0.888670</td>\n",
       "      <td>0.725329</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.548728</td>\n",
       "      <td>0.893414</td>\n",
       "      <td>0.717928</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.541192</td>\n",
       "      <td>0.895169</td>\n",
       "      <td>0.719984</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.531634</td>\n",
       "      <td>0.888082</td>\n",
       "      <td>0.722862</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.524785</td>\n",
       "      <td>0.894039</td>\n",
       "      <td>0.714638</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.514128</td>\n",
       "      <td>0.897363</td>\n",
       "      <td>0.717928</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.506098</td>\n",
       "      <td>0.891743</td>\n",
       "      <td>0.720806</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.502841</td>\n",
       "      <td>0.895226</td>\n",
       "      <td>0.724095</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.497458</td>\n",
       "      <td>0.896422</td>\n",
       "      <td>0.718750</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.493756</td>\n",
       "      <td>0.897386</td>\n",
       "      <td>0.719572</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.490881</td>\n",
       "      <td>0.893916</td>\n",
       "      <td>0.723684</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.490241</td>\n",
       "      <td>0.890163</td>\n",
       "      <td>0.725329</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.484755</td>\n",
       "      <td>0.895402</td>\n",
       "      <td>0.721628</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.477224</td>\n",
       "      <td>0.893949</td>\n",
       "      <td>0.724507</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.472201</td>\n",
       "      <td>0.892231</td>\n",
       "      <td>0.722039</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.472516</td>\n",
       "      <td>0.894397</td>\n",
       "      <td>0.723273</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.475461</td>\n",
       "      <td>0.894713</td>\n",
       "      <td>0.723273</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.478181</td>\n",
       "      <td>0.897070</td>\n",
       "      <td>0.722039</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.479460</td>\n",
       "      <td>0.893494</td>\n",
       "      <td>0.724095</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.481154</td>\n",
       "      <td>0.895368</td>\n",
       "      <td>0.722451</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.722 +/- 0.003 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# self-supervised: fine-tuning with 100% labels\n",
    "n_epochs = 50\n",
    "freeze_epochs = 10\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls100, InceptionTimePlus, pretrained=True, weights_path=f'data/MVP/{dsid}_200.pth', metrics=accuracy)\n",
    "    learn.fine_tune(n_epochs, base_lr=2e-2, freeze_epochs=freeze_epochs)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.717 +/- 0.003\n",
      "weights from data/TSBERT/LSST_200.pth successfully transferred!\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.414227</td>\n",
       "      <td>2.159549</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.092925</td>\n",
       "      <td>1.750759</td>\n",
       "      <td>0.418997</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.809917</td>\n",
       "      <td>1.468742</td>\n",
       "      <td>0.520148</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.574563</td>\n",
       "      <td>1.276300</td>\n",
       "      <td>0.583470</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.390574</td>\n",
       "      <td>1.160002</td>\n",
       "      <td>0.609375</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.248956</td>\n",
       "      <td>1.083413</td>\n",
       "      <td>0.634046</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.147548</td>\n",
       "      <td>1.007189</td>\n",
       "      <td>0.672286</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.075857</td>\n",
       "      <td>1.004348</td>\n",
       "      <td>0.668997</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.009742</td>\n",
       "      <td>0.970522</td>\n",
       "      <td>0.682566</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.964127</td>\n",
       "      <td>0.940699</td>\n",
       "      <td>0.691612</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.859984</td>\n",
       "      <td>0.930507</td>\n",
       "      <td>0.694490</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.847371</td>\n",
       "      <td>0.921850</td>\n",
       "      <td>0.698602</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.842118</td>\n",
       "      <td>0.920767</td>\n",
       "      <td>0.696546</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.843687</td>\n",
       "      <td>0.910492</td>\n",
       "      <td>0.703536</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.833807</td>\n",
       "      <td>0.923794</td>\n",
       "      <td>0.701891</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.819557</td>\n",
       "      <td>0.906354</td>\n",
       "      <td>0.700247</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.805423</td>\n",
       "      <td>0.914514</td>\n",
       "      <td>0.700247</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.795474</td>\n",
       "      <td>0.906764</td>\n",
       "      <td>0.708059</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.784673</td>\n",
       "      <td>0.910392</td>\n",
       "      <td>0.706003</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.763779</td>\n",
       "      <td>0.899414</td>\n",
       "      <td>0.706414</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.751464</td>\n",
       "      <td>0.890051</td>\n",
       "      <td>0.712171</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.735793</td>\n",
       "      <td>0.889761</td>\n",
       "      <td>0.710938</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.720709</td>\n",
       "      <td>0.880682</td>\n",
       "      <td>0.716694</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.709136</td>\n",
       "      <td>0.882319</td>\n",
       "      <td>0.712993</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.695246</td>\n",
       "      <td>0.882351</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.682864</td>\n",
       "      <td>0.880412</td>\n",
       "      <td>0.713405</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.677872</td>\n",
       "      <td>0.881668</td>\n",
       "      <td>0.713405</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.672824</td>\n",
       "      <td>0.876280</td>\n",
       "      <td>0.715461</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.672302</td>\n",
       "      <td>0.877617</td>\n",
       "      <td>0.716283</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.671228</td>\n",
       "      <td>0.878940</td>\n",
       "      <td>0.716283</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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dXEFIKaWKydPK+VylrHxOBez4HjAQeV3u+/2DbHlAXIzt46vyFhdj519lpsPQd+GhTbbu2EMSZNAkuWACysMV99v/gP1fgpR4+PwG+OBa+GNh3snyzkXwdhfbsqT9bXD/GmgxtGh1SpED4M4fwdffJunOXALz0O+w4DF4pRl8fB283NQ2Rt+5SNejV8qDiUg/EdkhIrtEZHIu+18VkY2Oxx8ikpJt3w8ikiIi83Ic87GIxGU7rkhN6AMDA0lOTi71CaQxhuTkZAIDvbD1qCq82PlQuT7UyKfFZfQdEFQVYl4qubi8zZHtMOtmuw7GnYug7ShbruJhtNyiMPyDoPPd9quAjTNg+X9hxk22TVu3R6HZdbaJ+Kkj8MNkm8hWbwZjf4D6VxT/+jVawLjF8MXNNok9Emu/8ilK4/JzJ+D3OXbmaOIG8A2AqEEQORD2rbD7tsyFkBq2cL7NKKjZsvifQSnlFCLiC7wN9Ab2A2tF5FtjzJ/LTxljJmZ7/4NAu2yneAkIBu7J5fSPGmPmFCe+8PBw9u/fT1JSUnFO4xUCAwMJDw93dxjK1c6nwp4ldk2G/Aa7sgbWfvm3/fe1dru831sWnTwInw+zOdWYOR69crImyUXhVw6ix9om1pu/gGWv2MQ1rLlNNFe/Z1uWdP8nXD3Bvt9ZyleHW7+BeZNszVNSrK1bLkgzbWNg3682Md76NaSfhbAWdgJh65su9ntseYMtL9n5I2ycCavftaPhNVvZZLnVcNuL2tky0mzLvkp1dVawUpfXCdhljNkDICKzgCFAXmu0jgKezHphjPlZRLq7Kjh/f/9LVjtUyuvt+hkyzuddapFdp7vtqnAxL8PI6a6PzVucT7WDi2ePwx3fF7z01E203KI4fP1tonz/WrjhfdtebukLNlm+dzl0/7tzE+QsfuVgyFvQ5zmInWf7KZ/Yn/f7Uw/D8lfhrWj4eABsnwdtRsK4X+C+FbbPdFaCnP0aUYPsgiYP/2FXtvHxg4X/hFciYfpNsOVLW8dcWJmZcCwOYhfYG8icO2HKlfBcLXitFUztbme6lvKvaZX7hYSEAJCYmMiwYcNyfU/37t1xaVvKoqsDJGR7vd+x7S9EpD4QAfxSwHM/JyKbHeUaud7ERORuEVknIuvKwmixUsTOh+BqULcAna4CK0Lne+2/0Ye3uj42b5CRDv+73f553PSJVyyWpiPJzuDrZ0diW95ok7+qDV2/drsIXPkAVG8Kc++EqT3s5L66He3+jHTYtQh++8wuk2kyoN6VdunM5kMKt4xj+Wp2sZbO99gSj00zYfNs2LkQylWCltdDm9FQt9OlI8DG2MmKR7bZ+qM/n2Mh7fTF91WuZ3+xaNrH3oDWTIXPb4T6V0HP/3NOqYpS+ahduzZz5hSrusDTjQTmGGMyCvDefwCHgABgKvB34JmcbzLGTHXsJzo6Wn+jVaVbRpqdgxQ1qOArvXW+17aSXfaKXSWuLDMG5k+0A2CDXocmvd0dUYFokuxMPr5QvXHJXrNpH1v0PnOEnXTX9zk4mWhrpk8dgvJhNpludwtUb1L864VFQu+n7fLZcUvtkuCbZ8P6j+0vB82H2q9TspLis8cuHls+DMKioP2t9jmsOYQ2s79xZ9fpHvjtEzvp4aN+dqWdno/bpciVysfkyZOpW7cu999/PwBPPfUUfn5+LF68mOPHj5OWlsazzz7LkCFDLjlu7969DBw4kC1btnD27FnGjh3Lpk2biIyM5OzZs+74KAVxAMj+XWW4Y1tuRgL3F+SkxpiDjh/Pi8hHwCNFjlCp0mLvcjh/omClFlmCq9r65RWv2/lDzvg32FstewV++xS6PgIdbnd3NAWmSXJpEBZpJ/TNvhUWPALiA0362MS4aV9bFuJsPr7QqKd9nE+1y4FvmmknM5araJPg5oNtIpyVEJevXrBz+wXYRVbajrGjyiteg6nX2AS8x78gtKnzP49yvu8n284pzlSzFfR/Ps/dI0aMYMKECX8mybNnz2bhwoWMHz+eihUrcvToUbp06cLgwYORPOre33nnHYKDg9m+fTubN2+mffv2zv0MzrMWaCIiEdjkeCQwOuebRCQSqAKsLMhJRaSWMeag2D+gocAW54WslJeKnQ/+wdCoR+GOu+IBO09p2X/h+ndcE5un2/SFncTYeoQd8PIimiSXFsFV4eYvYfcvUKt1ya5SU64CtBtjH+dP2VIOZ0y8Cwi2Ex+jx9qvrFa+Ddu/tZMHr/k7VKlf/GuoUqVdu3YcOXKExMREkpKSqFKlCjVr1mTixInExMTg4+PDgQMHOHz4MDVr1sz1HDExMYwfPx6A1q1b07p165L8CAVmjEkXkQeAhYAvMM0Ys1VEngHWGWO+dbx1JDDL5OjFJiLLgEggRET2A3caYxYC00UkFBBgI3BvCX0kpTyTMTZJbtSz8KvehoTaf8NWv2cXKataxiaz7lkK39wPDbrC4Le8blJ+gZJkEekHvI69EX9gjHk+x/76wDQgFDgG3GyM2e/YdxuQ9avDs8aYT5wUu8rJLwCa9XNvDOVCnH/OwErQ4592tvDyV2HN+7bEI3qs/eqmQg3nX1MVXz4jvq40fPhw5syZw6FDhxgxYgTTp08nKSmJ9evX4+/vT4MGDTh3rggTTj2QMWYBsCDHtidyvH4qj2NzXQ7MGNPTWfEpVSokboDURIh84vLvzc2V4+3qvStes/W4ZcXhbfDFLbYX8ojPPbIP8uVcdnZZtl6c/YHmwCgRaZ7jbS8DnxpjWmMnePzHcWxVbMuhzth2RU+KSBXnha/KlPLVbc31+A22q8i6afB6G1j0JJw5dvnjVZkwYsQIZs2axZw5cxg+fDgnTpwgLCwMf39/Fi9ezL59+/I9vlu3bsyYMQOALVu2sHnz5pIIWynlqWLng/ja8sWiqFjLlj9umJ5/J6rS5ORBmD7cK3oh56cgLRj+7MVpjLkAZPXizK45F1sLLc62vy+wyBhzzBhzHFgEuHmoU3m9SnVg0Gt2BcOoQXZSxOttYOlLtj5alWktWrQgNTWVOnXqUKtWLcaMGcO6deto1aoVn376KZGRkfkef99993Hq1CmioqJ44okn6NChQwlFrpTySLHzof6Vf22VWhhXTwAMrHjDaWF5rPOpMGO47YU8ZrbH90LOT0HKLXLrxZmzSeAm4AZsScb1QAURqZbHsbn28VSq0Ko1ghvftzefX56Dxc/ahU8632uXBS1fzd0RKjf5/feLEwarV6/OypW5z1k7deoUAA0aNGDLFjs/LSgoiFmzZrk+SKWU50veDUnbocMLxTtP5Xp2fYLfPrGtWEtrmWBGGsy+zZZajP7CK3oh58dZzXwfAa4RkQ3ANdiZ1gXpxwloU3pVTDVa2EVP7vrZtolb/Cy82gLmTYSjO90dnVJKKW8VO98+Rw4o/rmungQZF2Dlm8U/lycyBuZPgt0/w8D/ek0v5PwUJEm+bC9OY0yiMeYGY0w74F+ObSkFOdbx3qnGmGhjTHRoaGghP4JSDuHRcPNc+NsqaD3c1n+9FQ0zRkBcjK7gp5RSqnBi50PN1nYkuLiqNYKWw2DtNDidXPzzeZplL3tlL+T8FCRJ/rMXp4gEYNsJfZv9DSJSXUSyzvUPbKcLsK2J+ohIFceEvT6ObUq5TlgUDH4TJm61Ddz3r4NPBsF7Xe3iJ+kX3B1hqWbKyC8jZeVzKlVmnToCCashcqDzztn1YUg7A6umOO+cnmDTF/DLs17ZCzk/l02SjTHpQFYvzu3A7KxenCIy2PG27sAOEfkDqAE85zj2GPBvbKK9FnjGsU0p1wsJhe6TbbI8+E1bK/XVPfBaK7v6j3bEcLrAwECSk5NLfQJpjCE5OZnAwEB3h6KUcpUd3wOmcKvsXU5YpF1oa81UOJvivPO6k5f3Qs6PeNo/ZtHR0WbdunXuDkOVRsbArp9h5VuwZ7FdPantGOhyn/0aTBVbWloa+/fvLzV9iPMTGBhIeHg4/v6XrmgpIuuNMdFuCsst9L6tSqXpN0FSLDy0ybmJ36Hf4d2r7Qqy1zzmvPMWxbmT9jMW1dnjMHecbXN3x0KvbPWW3z1bV9xTZYcINLnWPg5vhZVT7EzjtR9AswFwxf22zU8p+i24pPn7+xMRUcZWlFJKlT7nU2HPEuh4l/P/TajZCpr2tyUXXe6zq9a6w4UzMPUaOLaneOcJqenVvZDzo0myKptqtIChb0OvJ2ySvPYD2OGYoFG9iW0c7+MHPj72+c/Xvvbx5+sc2/zKQeNr7TmUUkp5p10/Q8Z555ZaZNftUfigJ6z90NFD2Q1iXrIJ8sBXizcxsWYbW95YCmmSrMq2CjWg57/g6omw+QvY8Dkc3ASZGfZhMiAz3fE6/a/bTB6dDhtfC53vg0Y9baKtlFLKe8TOh+BqUDfnshBOEt7B/vuw8i3odDcEBLvmOnk5vA1+fQPajLbrCqhcaZKsFNgbVPRY+ygMY8BkXkyazx6HjTPsyPT0G6FaE+h8D7QZBeVCXBO7Ukop58lIgz8W2hVdfV2YJnV7DD7qB+s/suV+JSUzE+ZNgHIVoc+zJXddL6RDXEoVh4gttfArZxPtSnXgmkdhwu9wwwcQWBEWPAL/jYIf/gnH4twdsVJKqfzsXQ7nT7iu1CJL/SugYQ9Y/P/syn4l5bdPbGu7Ps/qyrSXoUmyUq7gF2AXNBn3i10JsEkfWPMevNEOZo62LXM8rLOMUkopbKmFfzA06uH6aw15y85tmXNHyfTwTz0MPz0J9a+GtqNdfz0vp0myUq4WHg3DPoQJW6DbI/Y3+E8HwztXwvqP7QxjpZRS7meMTZIb9QT/INdfr1I4DHkbDm6En592/fUW/hPSztrJetrJ6bK0JlmpklKxll2JqOsjsGUurH4HvnsIfnrKLuHZ8S57w8yNMbYl0ZlkuwjKmeTcH2eP23o6e9Clx+e5Lcf2wMoQUsPOVg6pYR/ls/0cVMV5kxHTL8D5k3DuhH0+nwpVG9myFaWUKmmJGyA1ESKfKLlrRg209/+Vb0HD7tCkt2uus+sn2DIHrpkMoU1dc41SRpNkpUqafyC0G2O/6opfCavegRWvw4o3IHIAlA/LlvhmS4gz03I/n4+fnYUdXA2CqkJA+Yv7LhkpkLy3ZW03mXYVqOTdcOqwbYGUk/hCSNiliXNWQl0+1E5iPHfCNqk/73jOSoJz/px+NvfPVLURRHSFiG52FaeQsIL8ySqlVPHEzrf3uKZ9S/a6fZ6FfSvhq3vhvhVQoaZzz3/hDMybBNUa225OqkA0SVbKXUTs4iX1r4SUeNsRY+MMO8KblfRWjbCtgrJeX/Koap/LVXTN12bG2GT21BHH47B9Pp3t51NH7MIsp4/Y5DgnvyA7eTGwko0zsKIdLQ+sZH8uV+nS/QHB9nxxMfD7XFuOAhAaZRPmiG7Q4Co7mu0pjIELpxy/GJxwdzRKqeKInW/vycFVS/a6/kEwbBpM7Q5f3QM3f+Xc9qExL0HKPrhtnh2oUQWiSbJSnqByPej9jH14ChFHMlvp8oujZGbCuRSbNPv6X0x6/QIKf92G3W07pIx027M6bqlNmn/71E5+RKBWazvCHHGNnSFenBWrjIG0MzbBPZtyMdm95JGS4znHw2QW/fpKqaI7lWSTPmesWpe8G5K2Q4cXin+uogiLhP7P2zK8X1933ohvVk/ktmPsN3SqwDRJVkoVn4+PY2TbiaMvvn52FD28A3SdBOnn4cB6mzDHLYM1U20Nn/hCnfYXSzPKVXAkuym5JLl5JMJ5lbJk8Q+++AtDYCVbWlK92aXbsh5PX++8PwOlVN4yM+H9nrYsbPBb0LRP8c4XO98+Rw4ofmxF1f422L0YfnnW3s/Co4t3vuw9kXv/2zkxliGaJCulvINfuYvlKd0n2xq7/WscSXMMLH8Nlr2S+7G+AXZCYlYiG1TFlrJckuBWdpR+VL743qDKRR8RV0q51oF1cCLe/v88Y7hNMPs+V/RR5dj5ULN18ZZoLi4RGPQ6HPgN5oyFe5fbe1FRZfVEHvqO9kQuAk2SlVLeKSDYlmY07G5fn0+FhDV25cOgypcmwCXRykkpVbJi59mJy/evsd8qrXgD9iyxCWGDqwp3rlNHbDLZ/R8uCbVQgirbtqHT+quKCYwAACAASURBVMF3E2ytclHmnWT1RG7Q1a76qgpN+yQrpUqHchWgcS/7lWvdThDazM4Q1wRZqdLHGNg+72L3m97PwNjvbTL58XWw8F+Qdq7g59vxPWBcv8peQdXtBD3+CVu/hA2fF+0c2hO52DRJVkoppZR3OfoHHNt9aVJb/wq4dwVEj7Ujy1OvgcSNBTtf7HyoXB9qtHBNvEVx9UQ71+L7xyBpR+GOzeqJ3PXhy0+8VnnSJFkppZRS3iV2nn1ulmOSXbkQO3I6Zq6dlPtBL1j6ou2Wk5fzqbZMI3KgZ424+vjC9VPtt2Fz7iz4yLj2RHYaTZKVUkop5V1i50Pt9nmvztnkWrjvV2g+FBY/Bx/2hqQ/cn/vrp9thwxPKbXIrmItW2N9+HdYVMBVALN6Ig98zU54VkWmSbJSSimlvMfJRNsO8nJJbXBVOwFu+MdwfC+81xVWvWvbomUXO98uzFS3s6siLp6mfaHL32yf+NgF+b9XeyI7lSbJSimllPIeOxyJYuTAgr2/xfXwt1W2E84Pf4dPB9tVTgEy0uCPhdC0v+3N7qmufcq2p/vmb/aXhNxoT2Sn0yRZKaWUUt4jdj5UbWQ72BRUhRowahYMfhMSN8CUK2HDdNi7DM6f8MxSi+z8ysGwjyD9AswdZ1td5pTVE7nvc9oT2Uk0SVZKKaWUdzibYhcPiryu8JPsRKD9rXDfCru0/Td/sxPi/IOhUQ/XxOtM1RvDdS/DvuV/XThJeyK7hCbJSimllPIOu36CzPSCl1rkpkoDuG0e9HkOLpyGpv28p596m1HQ6iZY8h+IX3Vxu/ZEdgkPLsBRSimllMomdh6UD4Pw6OKdx8cHrnwAWo/wngQZbAJ83Suwfy3MvQvuXWYnMW6ZY1cL1J7ITuVxI8m7jpxiTdwxd4ehlFJKKU+Sfh52LoJm/W0PYWcICbW9lb1JYEXbtSP1IHx9P8x/WHsiu4jHJcnpmYab3lvJxC82ciS1EEtKKqWUUqr0iouBC6eKV2pRWtTpAL2ehB3zbXs77YnsEh5XbtG0RgVG92jM1Jg9/LTtMBN7N+XWK+rj5+tx+bxSSimlSkrsPAgIsUs1K7jiAbtcdUiY9kR2EY/LPH0EHunbjIUTu9GufhWembeNgW8u1xIMpZRSqqzKzLQLaTS+FvwD3R2NZ/DxgaFvw7VPujuSUsvjkuQsEdXL88nYjrx7cwdSz6Vz03srmaQlGEop9ScR6SciO0Rkl4hMzmX/qyKy0fH4Q0RSsu37QURSRGRejmMiRGS145xfiEhASXwWpfJ1YB2cPqKlFqpEFShJLsCNuJ6ILBaRDSKyWUQGOLY3EJGz2W7S7xYmOBGhX8ua/DTpGh7o0Zh5mw/S6+WlTFseR3pG5uVPoJRSpZSI+AJvA/2B5sAoEWme/T3GmInGmLbGmLbAm8CX2Xa/BNySy6lfAF41xjQGjgN3uiJ+pQoldh74+EGT3u6ORJUhl02SC3IjBh4HZhtj2gEjgSnZ9u3OukkbY+4tSpBBAb5agqGUUpfqBOwyxuwxxlwAZgFD8nn/KGBm1gtjzM9AavY3iIgAPYE5jk2fAEOdGbRShWYMbJ9nF8oIquzuaFQZUpCR5ILciA1Q0fFzJSCPhcWLR0swlFLqT3WAhGyv9zu2/YWI1AcigF8uc85qQIoxJr0A57xbRNaJyLqkpKRCBa5UoRz9A47t9vylo1WpU5AkuSA34qeAm0VkP7AAeDDbvghHGcZSESn29Mu8SjA+WqElGEoplYeRwBxjTIazTmiMmWqMiTbGRIeGhjrrtEr9VayjbL7ZAPfGococZ03cGwV8bIwJBwYAn4mID3AQqOcow5gEzBCRijkPLsqIRFYJxg8TutK2XmWe/s6WYKzek4wxxkkfSymlPNYBoG621+GObbkZSbZSi3wkA5VFJKs9aH7nVKpkxM6H2u2hUq5faijlMgVJkgtyI74TmA1gjFkJBALVjTHnjTHJju3rgd1A05wXKM6IRMPQED69oxPv3tye1HPpjJi6iiv+8wsTZm1g1pp49h49rUmzUqo0Wgs0cXSjCMAmwt/mfJOIRAJVgJWXO6GxN8vFwDDHptuAb5wWsVKFdTLRLruspRbKDQqymMifN2JscjwSGJ3jPfFAL+BjEYnCJslJIhIKHDPGZIhIQ6AJsMdp0TvYEoxadGsaytcbElm5J5kVu5P5eqMtja5ZMZAuDavSpWE1ujSsRv1qwdj5KUop5Z2MMeki8gCwEPAFphljtorIM8A6Y0xWwjwSmGVyjBaIyDIgEghxlMrdaYxZCPwdmCUizwIbgA9L6CMp9Vc7Fthnbf2m3OCySXIBb8QPA++LyETsJL7bjTFGRLoBz4hIGpAJ3GuMcVlLiuAAP0Z3rsfozvUwxrDn6GlW7Ulm1Z5jLN/lmqT5QnomKWcucPxMGsfPXOB8eiadI6oS6O+kdeWVUioPxpgF2Hkg2bc9keP1U3kcm+scEWPMHuyEbaXcL3Y+VG0Eoc3cHYkqgwq0LPXlbsTGmG3AVbkcNxeYW8wYi0REaBQaQqPQEMZ0ro8xht1JWUlzcp5Jc7t6VbiQnsnxMxc4fuYCKWfSOHb6wiWJcEq251Pn0/9y7dAK5bjz6gjGdK5HhUD/kv7oSimllPc7mwJxMdDlb6Df/io3KFCSXBqICI3DQmgcFsLNXfJPmnNTMdCPKuUDqBwcQPWQAJqEhVA5OIAqwf5ULm+fqwQHcC4tg49W7OX572N5e/Eubr2iPmOviqB6SLkS/LRKKaWUl9v1E2SmQ9Qgd0eiyqgykyTnlFfSvDXxBEH+vlR1JMRVgv2pFOSPn2/BG4H0iqrB5v0pvLt0N1OW7OaDZXGM6FiXcV0bUrdqsAs/lVJKKVVKxM6D8mFQJ9rdkagyqswmyTllT5qdoXV4ZaaM6cDupFNMXbqHmWvimb46nkGta3Ff98Y0q1nBKddRSimlSp3087BzEbQaBj7O6larVOHo3zwXaxQawgvDWrPssZ6MvbIBP247TN/XYrjz47Ws36fLaiullFJ/ERcDF05pVwvlVpokl5CalQJ5fGBzfp3ck4nXNuW3+OPc+M5Kbnp3JYt3HNFezkoppVSW2HkQEAIR3dwdiSrDNEkuYZWDA3jo2iasmNyTJwY2J+H4GcZ+tJYBbyzn202JurS2Ukqpsi0zE2IXQJPe4KeT3pX7aJLsJsEBftxxdQRLH+3By8PbkJaRyfiZG+j136Us3nHE3eEppZRS7nFgHZw+oqUWyu00SXazAD8fhnUI58cJ3Xjvlg4E+Pow9qO1/OPLzbn2YFZKKaVKtdh54ONvR5KVciNNkj2Ej4/Qt0VNvnvwau65piGz1ibQ77UYft191N2hKaWUUiXDGNg+DyK6QmAld0ejyjhNkj1MoL8v/+gfxZx7r8DPRxj9/mqe+nYrZy9kuDs0pZRSpc2qd2HvcndHcdHRP+DYboi8zt2RKKVJsqfqUL8qCx7qyu1XNuDjX/cy4I1lrN933N1hKaWUKi3Op8LCf8D0myBxg7ujsWLn2edmA9wbh1JokuzRggP8eGpwC2bc1ZkL6ZkMf/dXnv8+lvPpOqqslFKqmPavBZMJGJsoH9/n7oggdj7U6QAVa7s7EqU0SfYGVzauzg8TujKiY13eXbqbQW8uZ8uBE+4OSymllDeLXw3iA7d9BxnnYfowOOvGbyxPJsKB9VpqoTyGJsleokKgP/+5oTUfje1Iypk0hr69gtd++oM07auslFKqKBJWQVgLCI+GkTPg+F6YNcYuCe0OOxbYZ239pjyEJslepkezMH6c2I2BrWvx2k87uX7KCnYcSnV3WEoppbxJRjrsXwf1OtvXDa6GIVNg3wr4+j67oEdJi50P1RpD9aYlf22lcqFJsheqHBzAayPb8e7N7TmYco5Bby7n3aW7ycjUpa2VUkoVwJGtcOEU1O1ycVvr4dDrSdgyF355pmTjOZsCcTG21EKkZK+tVB40SfZi/VrWYuHEbvSMDOP572MZ/u6v7Ek65e6wlFJKebr41fY5ayQ5y9UTocNYWP4qrP2w5OLZ9RNkpmuphfIomiR7ueoh5Xjn5va8PrItu46cYsAby/hoRRyZOqqslFIqLwmroEJtqFT30u0iMOBlaNIXFjwCO34omXhi50H5MKgTXTLXU6oANEkuBUSEIW3rsGjSNVzRsBpPf7eNke+vIj75jLtDU0op5YniV9tR5NxKG3z9YNg0qNka5oyFA7+5Npb087BzEUQOAB9NS5Tn0L+NpUiNioFMu70jLw5rzfbEk/R7PYbPVu7VUWWllFIXndgPJ/dfWo+cU7kQGD0bgqvDjBGu7aEcF2Pro7XUQnkYTZJLGRHhpui6LJzYjQ71q/B/32zllmmr2X9cR5WVUkoB8avsc8565Jwq1ICb57i+h3LsPAgIgYhurjm/UkWkSXIpVbtyEJ/e0Yn/3NCKjfEp9H01hplr4jFGR5WVUqpMS1gN/uWhRqvLvze0GYyc6boeypmZELsAmvQGv3LOPbdSxaRJcikmIozqVI8fJnSjTd3K/OPL37nto7Ukppx1d2hKKaXcJX4VhHewtccF0eAqGPqOa3ooH1gHp49oqYXySJoklwF1qwbz+Z2d+feQFqyNO0bfV2OYvS5BR5WVUqqsOZ8Kh7fkX4+cm1bD4NqnnN9DOXYe+PjbkWSlPEwBf41U3s7HR7jligZ0axrKo3M289iczfyw5RD/uaEVNSoGujs8pZRSJWH/OjCZl69Hzs1VEyAl3vZQrlQXOt5Z+HNcOAOJv9mSj/jVsHeZXe0vsFLhz6WUi2mSXMbUr1aeWeO68PGve3lxYSy9/7uUp4e0YGjbOoiucqSUUqVbwmpAILxj4Y8Vgf4vwYkDtodyxTrQrF/+x5w8aHsyJ6yxZR6HNttFQwCqN4OWN9oFTJTyQJokl0E+PsIdV0fQvZkdVZ74xSYW/H6I565vSVgFHVVWSqlSK34V1GhR9JHbrB7KH19neyjfPh/qtLf7MjPg8FabiGeNFJ+It/v8gqBOB7jqIajb2SbpwVWd85mUchFNksuwhqEhzL7nCj5cvoeXf/yDPq/G8MyQlgxqXUtHlZVSqrTJzLDlFq1vKt55snoof3Ct7aHc4TbYv9ae+8Ip+54KtWwyfMXfoG4nuzCJr3/xP4NSJUiT5DLO10e4u1sjekaG8fD/NjN+5gamLY/jb90bcW1UDXx8NFlWSqlS4fBWuJAK9Qo5aS83WT2Up/WFZa9AjZbQZpQ9d91OtmZZB1uUl9MkWQHQOKwCc++9gi/WJfDu0t3c/dl6mtYI4d5rGjGoTW38fbURilJKebWE1fa5bhEm7eUmtBlM2m5rjMtVcM45lfIgBcp8RKSfiOwQkV0iMjmX/fVEZLGIbBCRzSIyINu+fziO2yEifZ0ZvHIuP18fxnSuz+KHu/PaiLYIwqTZm+jx8hI+W7mXc2kZ7g5RKaVUUcWvsmUQles575z+QZogq1LrskmyiPgCbwP9gebAKBFpnuNtjwOzjTHtgJHAFMexzR2vWwD9gCmO8ykP5ufrw9B2dfj+oa58cGs0YRXK8X/fbOXqFxYzZckuTp5Lc3eISimlCithtR1F1jIIpQqkICPJnYBdxpg9xpgLwCxgSI73GKCi4+dKQKLj5yHALGPMeWNMHLDLcT7lBXx8hGub12DufVcy6+4uNK9dkRd/2MFVz//CSwtjOXrKycuTKqWUco0TB+BEgnPqkZUqIwpSk1wHSMj2ej+Qs6DpKeBHEXkQKA9cm+3YVTmOrZPzAiJyN3A3QL16TvwaSDmFiNClYTW6NKzG7/tP8M7SXUxZspsPlsUxsmNdxnVrSHiVYHeHqZRSKi8Jjn+KnVWPrFQZ4KzZWKOAj40x4cAA4DMRKfC5jTFTjTHRxpjo0NBQJ4WkXKFVeCWmjOnAT5OuYUjb2sxYE0/3l5bw8OxN7DqS6u7wlCpTCjBf5FUR2eh4/CEiKdn23SYiOx2P27JtX+I4Z9ZxYSX1eZQLxa8G/2Co2crdkSjlNQoyknwAqJvtdbhjW3Z3YmuOMcasFJFAoHoBj1VeqFFoCC8Oa8OEa5vywbI4Zq6J58sN++nTvAbjezWhRW1dYlQpV8o2X6Q39lu6tSLyrTFmW9Z7jDETs73/QaCd4+eqwJNANLZcbr3j2OOOt48xxqwrmU+iSkTCKruYh/YqVqrACjLauxZoIiIRIhKAnYj3bY73xAO9AEQkCggEkhzvGyki5UQkAmgCrHFW8Mr9alcO4olBzVkxuScP9mjMyt3JXPfGcv42fT07D+vIslIuVJD5ItmNAmY6fu4LLDLGHHMkxotwDHSoUuj8KTi0ReuRlSqkyybJxph04AFgIbAd28Viq4g8IyKDHW97GBgnIpuwN+HbjbUVmA1sA34A7jfGaB+xUqhq+QAm9WnGsr/3ZHyvJsT8cZQ+r8UwYdYG4o6ednd4SpVGuc0X+cucDwARqQ9EAL8U8NiPHKUW/yd5LL8pIneLyDoRWZeUlFTUz6BKwoF1YDKgribJShVGgRYTMcYsABbk2PZEtp+3AVflcexzwHPFiFF5kUpB/kzq3ZSxVzbgvZg9fPxrHN9tPsiw9uE82KuxTvBTyj1GAnMKOEgxxhhzQEQqAHOBW4BPc77JGDMVmAoQHR1tnBmscrL41YBAeLS7I1HKq+gyasolqpQPYHL/SGIe68GtV9Tnq40H6PHyEh7/+ncOnTjn7vCUKg0KM+djJBdLLfI91hiT9ZwKzEDbdnq/hFUQFgVBld0diVJeRZNk5VJhFQJ5clALlj7anZui6zJrTQLdXlrMM99tIylV+ywrVQwFmS+CiEQCVYCV2TYvBPqISBURqQL0ARaKiJ+IVHcc5w8MBLa4+HMoV8rMgIS12vpNqSLQJFmViFqVgnju+lYsfqQ7Q9rU5uNf4+j24mJe+CGW46cvuDs8pbxOAeeLgE2eZxljTLZjjwH/xibaa4FnHNvKYZPlzcBG7Ojy+yXygZRrHNkOF1J10p5SRSDZ7pseITo62qxbp52HSrs9Sad4/eedfLspkfIBftx5dQR3do2gYqC2J1LeTUTWG2PKVPGn3rc92NoPYP7DMH4jVI1wdzRKeZz87tk6kqzcomFoCK+PbMcPD3Wja5PqvP7zTrq+sJi3F+/ifLo2QFFKKaeIXw0hNaBKA3dHopTX0SRZuVWzmhV45+YOzHvwajrUr8JLC3dw96frOZemibJSShVbwipbj5x7Jz+lVD40SVYeoWWdSky7vSPP39CKpX8kcc9nmigrpVSxnDwIKfFaj6xUEWmSrDzKyE71eOHGVsTsTGLcp+s0UVZKqaJKWGWfdRERpYpEk2TlcUZ0rMcLN7Rm+a6jmigrpVRRxa8GvyCo1drdkSjllTRJVh7ppo51efFGmyjf9ck6zl7QRFkppQolYRXU6QC+2jVIqaLQJFl5rOHRdXlpWBtW7D7KXZ+u1URZKeV8BzfB2ePujsL5LpyGg5uhni4iolRRaZKsPNqwDuG8PKwNv+5O5s5PNFFWSjnRmWPwfk+Y2gOOxbk7Guc6sB5MhtYjK1UMmiQrj3djh3D+e1MbVu1J5o6P13LmQrq7Q1JKlQZ7l0FmOpxMhGl94VApWoE7frV9rtvRvXEo5cU0SVZe4fp24fz3prasjtNEWSnlJHHLwL88jPsZfPzgowGw71d3R+UcCasgNAqCqrg7EqW8libJymsMbVeHV0e0ZU3cMcZ+pImyUqqY4mKg/hVQsxXcsRBCwuCz6yF2gbsjK57MTEhYq/XIShWTJsnKqwxpaxPltXuPcftHazl9XhNlpVQRpB6Cozsgopt9XbmuTZTDmsMXN8OGz90bX3EkbYfzJ7QeWali0iRZeZ0hbevw2sh2rNtrR5Q1UVZKFVrcMvuclSQDlK8Gt31nt31zPyx/zT2xFVe8YxERHUlWqlg0SVZeaXCb2rw+sh3r449z+0drOKWJslKqMOKWQmAlqJljoY1yITB6NrS4AX56En583JYveJOE1VA+DKpEuDsSpbyaJsnKaw1qU5s3Rrbjt/gUbp+mibJSqhDiYqBBV/Dx/es+vwC48UPodDf8+qYdVc5IK/kYiyp+lR1FFnF3JEp5NU2SlVe7rnUt3hzVjg0JmigrpQro+F5I2XdpqUVOPj7Q/0Xo8S/YNMPWKV84U2IhFlnqIfvZtB5ZqWLTJFl5vQGtavHWqHZsTEjhtmlrOH76grtDUkp5sqx65AZd83+fCFzzGFz3X/hjoe184emr8/1Zj6xJslLFpUmyKhX6t6rFW6PbsSkhhR6vLOGzVfvIyDTuDksp5YniYiC4OoRFFez9He+E4R9D4m+2l/LJgy4Nr1gSVoNf4F9rrZVShaZJsio1+rWsxfzxXYmqWZH/+3oLA99czuo9ye4OSynlSYyxK+1FdCtczW6LoTDmf5ASDx/2gaO7XBdjccSvgjodbF21UqpYNElWpUqzmhWYMa4zU8a05+TZNEZMXcWDMzeQmHLW3aEppTxB8i5IPZh/PXJeGnaH2+dB2hm7jHXiBmdHVzwXzsChzVBXW78p5QyaJKtSR0QY0KoWP026hod6NeHHrYfo9cpS3vx5J+fSMtwdnlLKneKW2ueiJMkAtdvZRUf8g+HjgbBnqfNiK64D6yEzXeuRlXISTZJVqRUU4MvE3k35adI1dG8WyiuL/qD3q0v5ceshjNF6ZaXKpLgYqBgOVRsW/RzVG8OdP0LlejB9GGz92nnxFUeCY9JeeEf3xqFUKaFJsir16lYN5p2bOzD9rs4E+fty92fruXXaGnYdSXV3aEqpkpSZaTtbFLYeOTcVa8HYBVC7Pfzvdlj7oVNCLJb41RAaCcFV3R2JUqWCJsmqzLiqcXXmj+/Kk4OaszEhhX6vLePZeds4ec6LFglQShXdka1w9ljRSy1yCqoCt3wFTfvC/Emw9EU7MdAdMjNh/xqtR1bKiTRJVmWKv68PY6+KYMkj3RkeHc6HK+Lo+fISZq9LIFNbxilVusXF2OeIy/RHLoyAYBjxObQZDYufg+8fc88y1kmxcO6E1iMr5UQFSpJFpJ+I7BCRXSIyOZf9r4rIRsfjDxFJybYvI9u+b50ZvFJFVS2kHP+5oTXf3n819aoG89iczVw/ZQUb4j18oQClVNHFxUDVRlAp3Lnn9fWHoVPgygdhzVT48i5IL+FFjbLqkXUkWSmnuWySLCK+wNtAf6A5MEpEmmd/jzFmojGmrTGmLfAm8GW23Wez9hljBjsxdqWKrVV4JebedyWvjmjDwRPnuH7Krzw7bxsX0t0wEqSUcp2MdNi7wnmlFjmJQJ9nofczsGUuzLgJzp9yzbVyE78ayocWb0KiUuoSBRlJ7gTsMsbsMcZcAGYBQ/J5/yhgpjOCU6okiAjXtwvnl0e6c0uX+nywPI7h760k4dgZd4emlHKWgxvhQqpzSy1yc9VDMORtO2r9ySA4XUILGiWssqPIxZ2QqJT6U0GS5DpAQrbX+x3b/kJE6gMRwC/ZNgeKyDoRWSUiQ/M47m7He9YlJSUVMHSlnCuknB//HtqSd8a0Z0/SKa57Yxk/bDnk7rCUUs6QVY/cwMVJMkC7m22d8pFtdtGRlITLH1McqYfh+F4ttVDKyZw9cW8kMMcYk33FhvrGmGhgNPCaiDTKeZAxZqoxJtoYEx0aGurkkJQqnP6tajH/wa5EVC/PvZ+v56lvt3I+XRchUcqrxcVAWHMICSuZ60UOsJ0vTh2xy1gfiXXdtbLqkXXSnlJOVZAk+QBQN9vrcMe23IwkR6mFMeaA43kPsARoV+golSph9aoF8797r+SOqyL4+Ne9DHtnJfuST7s7LKVUUaSfh/hVrqtHzkv9K20vZZNhR5QT1rjmOglrwLcc1GrjmvMrVUYVJEleCzQRkQgRCcAmwn/pUiEikUAVYGW2bVVEpJzj5+rAVcA2ZwSulKsF+PnwxKDmTL2lA/uSTzPwjeXM33zQ3WEppQpr/zpIP1vySTJAzZZ2db7gqvDpENi5yPnXiF8FddqDXznnn1upMuyySbIxJh14AFgIbAdmG2O2isgzIpK9W8VIYJa5dL3fKGCdiGwCFgPPG2M0SVZepU+Lmix4qCuNa4Rw/4zfePzr3zmXpuUXSnmNuBgQH6h/lXuuX6UB3LEQqjWGmSNh82znnTvtLBzcpPXISrmAX0HeZIxZACzIse2JHK+fyuW4X4FWxYhPKY8QXiWY2fdcwUsLdzA1Zg+/7Uvh7THtiahe3t2hKaUuJy7GliIEVXZfDCFhcPt8mDUavhwHZ5Khy33FP++B3yAzTeuRlXIBXXFPqQLy9/XhnwOimHZ7NIknzjLwjWV8szGv8nyllEe4cBr2r3VPqUVOgRVhzByIGgQ/TIafnyn+Mta6iIhSLlOgkWSl1EU9I2uwYHxXxs/cwEOzNrJqTzJPDmpBoL+vu0NTSuUUv8qOtHpCkgzgHwjDP4H5k2DZK3A6CXo+UfT+xntXQPWmtuZZKeVUmiQrVQS1Kwcx6+4u/HfRH0xZspsN8Sm8Nbo9jcNC3B2aKkNEpB/wOuALfGCMeT7H/leBHo6XwUCYMaayY99twOOOfc8aYz5xbO8AfAwEYcvsHsox18S7xMWAjx/Uu8LdkVzk4wsDX7Mr5MW8BL99Wrzztb/VOXEppS6hSbJSReTn68Nj/SLp3LAaE7/YyKA3l/Ps0Jbc2CHc3aGpMkBEfIG3gd7YRZ7Wisi32SdHG2MmZnv/gzhacIpIVeBJIBowwHrHsceBd4BxwGpsktwP+L5EPpQr7F0G4R0hwMPmD4hAz8ehbhc4Hle8c0UOdE5MSqlLaJKsVDFd0zSU7x+y5RcP/28TK3Yf5ZkhLQkpp/97KZfqBOxy9KBHRGYBQ8i7zeYobGIM0BdYZIw55jh2EdBPRJYAFY0xqxzbPwWG4q1J8rkTkLgBuj7i7kjy1uRaS+WfsAAAFlBJREFUd0eglMqDTtxTyglqVAxk+l2deahXE77ecIDr3ljGxoQUd4elSrc6QPb1jvc7tv2FiNQHIoBfLnNsHcfPBTnn3SKyTkTWJSUlFekDuNy+X8Fkek49slLKq2iSrJST+Pn6MLF3U7645wrSMwzD3vmVtxfvIiPTe8s5VakxEphjjHFag29jzFRjTLQxJjo0NNRZp3WuuBjwC7TlFkopVUiaJCvlZB0bVGXBQ13p17ImLy3cwej3V5GYctbdYanS5wBQN9vrcMe23IwEZhbg2AOOnwtyTs8XF2Nbo/kHujsSpZQX0iRZKReoFOTPm6Pa8fLwNvx+4AT9X1/G97/rktbKqdYCTUQkQkQCsInwtznfJCKRQBVgZbbNC4E+IlJFRKoAfYCF5v+3d+fhUdX3Hsff30wSgiFAwi4gCYgoggqGpZStYhVsXWstohW3ct16xV69xae9avFaW32qVWvxsYgKFyvuRUURN0JVlohsskNAQAQkERAEsvzuH+dEhzHLhJnMmcDn9TzzzJlzfufMJ4eTX76cOec3zm0FdptZfzMz4ArgX/X9g9SLvV/CtmW61EJEDpuKZJF6YmZcfHoHZvznIHJbHMP1Uxdy+0tL2HewLOhocgRwzpUBN+EVvCuA55xzn5rZeDM7L6zpSODZ8GHc/Bv27sYrtBcA4ytv4gNuACYCa4F1NNSb9jbM8Z7zhgSbQ0QaLN1+L1LPcltm8sL1A3hw1momzF7HvKJiHh7Zix7tmwUdTRo459wMvGHawufdEfH6rmrWnQRMqmJ+IdAjfikDUlQA6VlwbK+gk4hIA6UzySIJkOaPqTz12n7sO1DOhX//gH8UrKdCN/WJ1I+iAug0AEI6FyQih0dFskgCDejSkjduHsQZJ7bmnhkrGP3kfLbv3h90LJEjy+7PYedaXY8sIjFRkSySYNmZ6Tx2+en88cKeLNhQzPCH5vD28m1BxxI5chRVXo+sIllEDp+KZJEAmBmj+h3Ha78eRNumGVw7uZA7/rWM/aVxG8ZW5OhVVACNs6FNw7+0WkSCoyJZJEDHt27CyzcO4NqBeUz+aCPn/e3fLPysJOhYIg2Xc1A0G3IHQor+xInI4VMPIhKwRqkhfv/T7ky+ui9f7Svlor9/yI1TF7Jx596go4nUv3Xvwt6d8dteyQbYtUlDv4lIzFQkiySJwSe04r1bh3LzsK68u3I7Zz4wmz+8+iklew8GHU2kfnz+CUy5ECafB/t3x2ebRQXes65HFpEYqUgWSSKZjVK55ccnMPu2oVx8egee/nADg+9/j8dmr9P1ynLkmTsBUhvDjpXw3BVQXhr7NosKoEkbaHlC7NsSkaOaimSRJNS6aQb3XnQKb44dTJ/cHP70xkqG/WU2L3+yWWMry5Fh91ZY9hKcfiWc+xCsfw9eHetdU3y4nPOK5LzBYBa3qCJydFKRLJLETmiTxaQr+/DMr/qRnZnGLdMWc96j/+bDtV8GHU0kNoVPQEUZ9BsDvS6HIeNg0f/B7PsOf5s7VsHe7brUQkTiQkWySAMwoEtLpt84kL/+4jRK9pYyauI8rnpyPqu37Qk6mkjdlX4DhZOg2zmQ09mbN3QcnDoK3v8jLHrm8Lar65FFJI5UJIs0ECkpxgW92vPOfw3h9hEnUrixhOF/LWDci0v0rX3SsCx9HvbthP7XfzfPzLvsovNQmP5rWPde3be7oQCaHwfZuXEKKiJHMxXJIg1MRlqI/xjShYLbfsSVA/J4ceFmhtz/Pg/MWs3eA2VBxxOpmXPeDXttenpjGYdLTYdLJkPLbjDtl/DFsui3W1HhfdOeziKLSJyoSBZpoLIz07nj3O68/ZshnHFSax5+Zw1D7n+fVz7Zgovl5ieR+lQ0G7Yv984iV3VzXUYzuOx5aJQFU38Ou7ZEt91tS2H/VxofWUTiRkWySAPXqUUmj47qzcs3DKBDdmPGTlvEryYXsk2XYEgymjsBMltBj59V36ZZe7jsOTiwB565JLoxlCuvR84dFJ+cInLUU5EscoTodVw2L14/gN//5CTmrPmSHz8wm+cLN+mssiSPnetg9ZuQfw2kZdTctm1PuOTp6MdQLiqAFl2habv45RWRo5qKZJEjSCjFuHZQZ94cO5hubbO47YUlXPXUAj7/6pugo4nAvMcglA75V0fX/vhh0Y2hXF4KGz/U9cgiElcqkkWOQHktM5k25gfceW535q0v5uwHC3h2/mc6qyzB+eYr+GQq9LgYstpEv140Yyh//gkc/FpFsojEVVRFspkNN7NVZrbWzMZVsfxBM1vkP1ab2Vdhy0ab2Rr/MTqe4UWkeikpxlU/zOPNsYM4uX1Txr20lCsmzWdzyb6go8nR6JMpULoX+l9X93VrG0O5aLb3rOuRRSSOai2SzSwEPAqMALoDl5pZ9/A2zrlbnHOnOedOAx4BXvLXzQHuBPoBfYE7zSw7vj+CiNSkU4tMnrm2P3effzIfbyzh7AcLmDJ3o77eWhKnvAzmPQ6dBkK7U+u+fm1jKBcVeEPKZbaIR1oRESC6M8l9gbXOufXOuYPAs8D5NbS/FPinP302MMs5V+ycKwFmAcNjCSwidZeSYvzyB7nMHDuYXsdl8z+vLOOyifP4bKfOKksCrHoddn126JeH1FVqOlwyBVqdeOgYyqX7YdN8XWohInEXTZHcHtgU9nqzP+97zKwTkAe8W5d1zWyMmRWaWeGOHTuiyS0ih6FjzjFMuaYv917Uk6VbdnH2Xwt46oMinVWW+jV3AjTvBN1GxLadjKYw6rlDx1DevADK9qtIFpG4i/eNeyOBF5xz5XVZyTn3uHMu3zmX36pVqzhHEpFwZsalfY9j5i2D6ZOXw12vLmfkP+ay4cu9QUeTI9GWhfDZR9DvOkgJxb69Zu29LxupHEN55etgIeg0IPZti4iEiaZI3gJ0DHvdwZ9XlZF8d6lFXdcVkQRq37wxT1/Vh/suPoUVW3cz/KECJs5ZT7nOKks8zXsM0rO8USripW0P+MVkbwzleRPg2F7eWWYRkTiKpkheAHQ1szwzS8crhKdHNjKzE4Fs4KOw2TOBs8ws279h7yx/nogkATPjkvyOzLplCD/s0pL/fX0FFzz6AR9vLAk6mhwJdm+FZS95BXK8i9guZ8C5D3vTnfVV1CISf6m1NXDOlZnZTXjFbQiY5Jz71MzGA4XOucqCeSTwrAsbiNU5V2xmd+MV2gDjnXPF8f0RRCRWbZtlMHF0Pq8u2co9ry/nZxM+5KLe7Rk34kRaZ9XyzWgi1Sl8AirKoN+Y+tl+r8ugRRdofVL9bF9EjmqWbF8ukJ+f7woLC4OOIXLU2nugjL+9t5aJc9bTKDXEzcO6MnpALump+u6haJjZx865/KBzJFKV/XbpN/DgydCxP1xaxdjGIiJJoKY+W3/1ROQQmY1S+e3wE3nrliH0yc3mnhkrGPFQAQWrNfKM1MHS52HfztiGfRMRCZCKZBGpUl7LTJ68qi9PjM6nrMJxxaT5jJlcyKZija0stXDOG/atTU/IHRh0GhGRw6IiWURqNOykNswcO5jbzu7GnDVfcuYDs3lg1mq+OVinkR7laFI0G7Yv984imwWdRkTksKhIFpFaZaSFuPFHx/PurUM46+S2PPzOGs58YDYzlm4l2e5rkCQwdwJktoIePws6iYjIYVORLCJRa9esMY9c2otnx/QnKyOVG6Yu5PIn5rFm256go0my2LkOVr8J+ddAmkZGEZGGS0WyiNRZ/84teO3XA/nDeSezdPMuhj80h/GvLmf3/tKgo0nQ5k6AUDrkXx10EhGRmNQ6TrKISFVSQymMHpDLuacey/0zV/Hkh0VMX7yFC05rT5+8HPrk5pCTmR50TEmkb0pg0VTocTFktQk6jYhITFQki0hMcjLTufeinozqexz3zVzJ5LkbmfjvIgCOb92EPrk59M3LJr9TDh2yG2O6kevItXAKlO6D/tcFnUREJGYqkkUkLnp2aMaUa/qxv7ScpVt2Mb+omAUbinlt8ef8c/5nALRrlkGf3Bz65OXQNzeHrq2bkJKiovlwmdlw4CG8b0Od6Jz7UxVtLgHuAhyw2Dk3yp//Z+AnfrO7nXPT/PlPAUOAXf6yK51zi2oNU14G8x+HTgOh3amx/FgiIklBRbKIxFVGWsgrhHNzACivcKz6Yg8LNhQzf0Mxc9fvZPrizwFo1jiNPrnZ5Pvte7Zvpm/2i5KZhYBHgR8Dm4EFZjbdObc8rE1X4Hbgh865EjNr7c//CdAbOA1oBLxvZm8453b7q97mnHuhToFWvga7NsHw79XpIiINkopkEalXoRSj+7FN6X5sU0YPyMU5x2fF+74901y4oYS3V2wHICMthdM6NqdvXgv65ubQu1NzjklXN1WNvsBa59x6ADN7FjgfWB7W5lfAo865EgDn3HZ/fnegwDlXBpSZ2RJgOPDcYaeZOwGad4JuIw57EyIiyUR/fUQkocyMTi0y6dQik5/ndwRgx54DFPpnmhdsKOZv766hwkFqitGjfTP6+pdn5Odm0/wY3Qzoaw9sCnu9GegX0eYEADP7AO+SjLucc28Ci4E7zewvwDHAjzi0uL7HzO4A3gHGOecORL65mY0BxgB06dgONq2Bs++FlFBcfjgRkaCpSBaRwLXKasSInu0Y0bMdAHv2l/LxxpJvzzY/9cEGHi9YD8CJbbP8mwG9R5umGou3BqlAV2Ao0AEoMLOezrm3zKwP8CGwA/gIqPwKxduBL4B04HHgt8D4yA075x73l5PfpZUjPQt6XV6/P42ISAKpSBaRpJOVkcbQbq0Z2q01APtLy1m86SvmF3lnm19cuJkpczcC0KnFMd8Vzbk5tG2WQXoo5Wi4IXAL0DHsdQd/XrjNwDznXClQZGar8YrmBc65e4B7AMzsGWA1gHNuq7/uATN7Eri11iT7S6DXrZDRNIYfR0QkuahIFpGkl5EWol/nFvTr3AKAsvIKlm/d7RXNRcW8s2IbL3y8+ZB1QilGWshID6WQnppCWthz5XR6yA6Znx5KoXF6iCaNUslsFCKzUao3nZ5Kkwx/ulEqTfxlmf6yUDAF+QKgq5nl4RXHI4FREW1eAS4FnjSzlniXX6z3b/pr7pzbaWanAKcAbwGYWTvn3Fbzxuq7AFhWaxIH9BsTn59KRCRJqEgWkQYnNZTCKR2ac0qH5lw7qDMVFY51O76mcGMJX+0r5WBZBaXlFRwsr/hu2n8uLXccCJt3sKyCvQfKOFDmtf/mYDlfHyhj74EyKlx0eRqneUVzVkbiulTnXJmZ3QTMxLveeJJz7lMzGw8UOuem+8vOMrPleJdT3OYXxhnAHH/M6t3A5f5NfABTzawVYMAioPZBjzOaQU7nOP+EIiLBUpEsIg1eSorRtU0WXdtkxW2bzjn2l1Z8WzBXPu89WMbXB8q96bD5lfPej1uCqDLOAGZEzLsjbNoBv/Ef4W32441wUdU2z6hzkCat6ryKiEiyU5EsIlIFM6NxeojG6SFaZTWKer1HIi94OBqkNwk6gYhI3GnUfhERERGRCCqSRUREREQiqEgWEREREYmgIllEREREJIKKZBERERGRCCqSRUREREQiqEgWEREREYmgIllEREREJIJ5X8iUPMxsD7Aq6Bw1aAl8GXSIWiR7RuWLjfLFpr7zdXLOHVVfQad+O2bKFxvli83Rnq/aPjsZv3FvlXMuP+gQ1TGzwmTOB8mfUflio3yxSfZ8DZT67RgoX2yULzbKVz1dbiEiIiIiEkFFsoiIiIhIhGQskh8POkAtkj0fJH9G5YuN8sUm2fM1RMm+T5UvNsoXG+WLTWD5ku7GPRERERGRoCXjmWQRERERkUCpSBYRERERiRBYkWxmw81slZmtNbNxVSxvZGbT/OXzzCw3gdk6mtl7ZrbczD41s5uraDPUzHaZ2SL/cUei8vnvv8HMlvrvXVjFcjOzh/39t8TMeicwW7ew/bLIzHab2diINgnff2Y2ycy2m9mysHk5ZjbLzNb4z9nVrDvab7PGzEYnMN/9ZrbS/zd82cyaV7NujcdDPea7y8y2hP07nlPNujX+vtdjvmlh2TaY2aJq1q33/dfQqc+OS07123XLpD47/vnUZ9eFcy7hDyAErAM6A+nAYqB7RJsbgMf86ZHAtATmawf09qezgNVV5BsKvBbE/vPffwPQsobl5wBvAAb0B+YF+G/9Bd5g3YHuP2Aw0BtYFjbvPmCcPz0O+HMV6+UA6/3nbH86O0H5zgJS/ek/V5UvmuOhHvPdBdwaxTFQ4+97feWLWP4X4I6g9l9DfqjPjltO9dt1y6E+O/751GfX4RHUmeS+wFrn3Hrn3EHgWeD8iDbnA0/70y8Aw8zMEhHOObfVObfQn94DrADaJ+K94+h8YLLzzAWam1m7AHIMA9Y55zYG8N6HcM4VAMURs8OPs6eBC6pY9WxglnOu2DlXAswChicin3PuLedcmf9yLtAh3u8brWr2XzSi+X2PWU35/L7jEuCf8X7fo4T67MRQvx1GfXZs1GfHLqgiuT2wKez1Zr7foX3bxj/gdgEtEpIujP+RYS9gXhWLf2Bmi83sDTM7OaHBwAFvmdnHZjamiuXR7ONEGEn1B3mQ+69SG+fcVn/6C6BNFW2SZV9ejXeWqSq1HQ/16Sb/o8VJ1Xz0mQz7bxCwzTm3pprlQe6/hkB9dnyo346d+uzYqc+Okm7cq4GZNQFeBMY653ZHLF6I91HUqcAjwCsJjjfQOdcbGAHcaGaDE/z+tTKzdOA84PkqFge9/77HeZ/hJOWYiGb2O6AMmFpNk6COhwlAF+A0YCvex2PJ6FJqPiOR9L9PUrsk77OhARxnDanfVp99WNRn10FQRfIWoGPY6w7+vCrbmFkq0AzYmZB03num4XW2U51zL0Uud87tds597U/PANLMrGWi8jnntvjP24GX8T4eCRfNPq5vI4CFzrltkQuC3n9htlV+nOk/b6+iTaD70syuBH4KXOb/UfieKI6HeuGc2+acK3fOVQD/qOZ9g95/qcBFwLTq2gS1/xoQ9dlxoH47LtRnx0B9dt0EVSQvALqaWZ7/v9aRwPSINtOByjtSLwbere5gizf/WpgngBXOuQeqadO28no7M+uLty8T8gfBzDLNLKtyGu9GgWURzaYDV5inP7Ar7COqRKn2f4JB7r8I4cfZaOBfVbSZCZxlZtn+R1Nn+fPqnZkNB/4bOM85t6+aNtEcD/WVL/x6yQured9oft/r05nASufc5qoWBrn/GhD12bFnVL8dH+qzY8unPrsuor3DL94PvLt4V+PdQfk7f954vAMLIAPv4561wHygcwKzDcT7CGcJsMh/nANcB1znt7kJ+BTvrs+5wIAE5uvsv+9iP0Pl/gvPZ8Cj/v5dCuQn+N83E6/zbBY2L9D9h9fxbwVK8a6xugbvmsl3gDXA20CO3zYfmBi27tX+sbgWuCqB+dbiXRtWeRxWjh5wLDCjpuMhQfmm+MfXErxOtF1kPv/1937fE5HPn/9U5XEX1jbh+6+hP6r6N0R9dl0yqt+uex712fHPpz67Dg99LbWIiIiISATduCciIiIiEkFFsoiIiIhIBBXJIiIiIiIRVCSLiIiIiERQkSwiIiIiEkFFsoiIiIhIBBXJIiIiIiIR/h/mSkbmhjxtWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.717 +/- 0.003 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# self-supervised: fine-tuning with 100% labels\n",
    "n_epochs = 20\n",
    "freeze_epochs = 10\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls100, InceptionTimePlus, pretrained=True, weights_path=f'data/MVP/{dsid}_200.pth', metrics=accuracy)\n",
    "    learn.fine_tune(n_epochs, base_lr=2e-2, freeze_epochs=freeze_epochs)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train 🏃🏽‍♀️🏃🏽‍♀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.606 +/- 0.011\n",
      "weights from data/TSBERT/LSST_200.pth successfully transferred!\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.633718</td>\n",
       "      <td>2.609508</td>\n",
       "      <td>0.035362</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.610511</td>\n",
       "      <td>2.557800</td>\n",
       "      <td>0.090872</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.574366</td>\n",
       "      <td>2.470443</td>\n",
       "      <td>0.336760</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.531439</td>\n",
       "      <td>2.329933</td>\n",
       "      <td>0.415296</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.465188</td>\n",
       "      <td>2.121298</td>\n",
       "      <td>0.417352</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2.379660</td>\n",
       "      <td>1.868411</td>\n",
       "      <td>0.426809</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2.276030</td>\n",
       "      <td>1.675144</td>\n",
       "      <td>0.438322</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.165751</td>\n",
       "      <td>1.546646</td>\n",
       "      <td>0.458470</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.042748</td>\n",
       "      <td>1.501567</td>\n",
       "      <td>0.519326</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.937585</td>\n",
       "      <td>1.382883</td>\n",
       "      <td>0.561266</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.829618</td>\n",
       "      <td>1.311114</td>\n",
       "      <td>0.577714</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.725061</td>\n",
       "      <td>1.264270</td>\n",
       "      <td>0.602385</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.614445</td>\n",
       "      <td>1.396313</td>\n",
       "      <td>0.579359</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.514647</td>\n",
       "      <td>1.412332</td>\n",
       "      <td>0.600329</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.411660</td>\n",
       "      <td>1.522774</td>\n",
       "      <td>0.606908</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.317945</td>\n",
       "      <td>1.502419</td>\n",
       "      <td>0.627056</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.223022</td>\n",
       "      <td>1.451829</td>\n",
       "      <td>0.629934</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.141247</td>\n",
       "      <td>1.474222</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.064253</td>\n",
       "      <td>1.444943</td>\n",
       "      <td>0.642270</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.993081</td>\n",
       "      <td>1.365548</td>\n",
       "      <td>0.648026</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.925187</td>\n",
       "      <td>1.617708</td>\n",
       "      <td>0.623355</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.862290</td>\n",
       "      <td>1.567623</td>\n",
       "      <td>0.632812</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.802516</td>\n",
       "      <td>1.532727</td>\n",
       "      <td>0.635691</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.746291</td>\n",
       "      <td>1.643754</td>\n",
       "      <td>0.621711</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.694662</td>\n",
       "      <td>1.611224</td>\n",
       "      <td>0.623355</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.646902</td>\n",
       "      <td>1.566386</td>\n",
       "      <td>0.628289</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.602784</td>\n",
       "      <td>1.640422</td>\n",
       "      <td>0.618832</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.561870</td>\n",
       "      <td>1.647204</td>\n",
       "      <td>0.610197</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.523730</td>\n",
       "      <td>1.570367</td>\n",
       "      <td>0.598273</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.487990</td>\n",
       "      <td>1.549082</td>\n",
       "      <td>0.606497</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.455023</td>\n",
       "      <td>1.537486</td>\n",
       "      <td>0.611842</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.424745</td>\n",
       "      <td>1.533999</td>\n",
       "      <td>0.624178</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.396629</td>\n",
       "      <td>1.542565</td>\n",
       "      <td>0.625822</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.370451</td>\n",
       "      <td>1.554239</td>\n",
       "      <td>0.626645</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.346070</td>\n",
       "      <td>1.565936</td>\n",
       "      <td>0.622122</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.323546</td>\n",
       "      <td>1.573626</td>\n",
       "      <td>0.620066</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.302723</td>\n",
       "      <td>1.584698</td>\n",
       "      <td>0.620477</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.283292</td>\n",
       "      <td>1.589390</td>\n",
       "      <td>0.618010</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.265295</td>\n",
       "      <td>1.592377</td>\n",
       "      <td>0.624178</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.248439</td>\n",
       "      <td>1.595517</td>\n",
       "      <td>0.621299</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.232787</td>\n",
       "      <td>1.596380</td>\n",
       "      <td>0.623355</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.218187</td>\n",
       "      <td>1.600643</td>\n",
       "      <td>0.621711</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.204541</td>\n",
       "      <td>1.608235</td>\n",
       "      <td>0.621711</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.191864</td>\n",
       "      <td>1.612765</td>\n",
       "      <td>0.621711</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.180077</td>\n",
       "      <td>1.616412</td>\n",
       "      <td>0.622122</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.168991</td>\n",
       "      <td>1.626840</td>\n",
       "      <td>0.620888</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.158625</td>\n",
       "      <td>1.631155</td>\n",
       "      <td>0.619655</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.148972</td>\n",
       "      <td>1.626844</td>\n",
       "      <td>0.618010</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.139889</td>\n",
       "      <td>1.614828</td>\n",
       "      <td>0.621711</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>1.613595</td>\n",
       "      <td>0.622122</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<IPython.core.display.HTML object>"
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.608 +/- 0.012 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# self-supervised: train with 10% labels\n",
    "n_epochs = 50\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls010, InceptionTimePlus, pretrained=True, weights_path=f'data/MVP/{dsid}_200.pth', metrics=accuracy)\n",
    "    learn.fit_one_cycle(n_epochs, 1e-2)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 accuracy: 0.728 +/- 0.006\n",
      "weights from data/TSBERT/LSST_200.pth successfully transferred!\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.403289</td>\n",
       "      <td>2.202990</td>\n",
       "      <td>0.416941</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.079202</td>\n",
       "      <td>1.615348</td>\n",
       "      <td>0.491776</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.691875</td>\n",
       "      <td>1.229404</td>\n",
       "      <td>0.579359</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.366223</td>\n",
       "      <td>1.009176</td>\n",
       "      <td>0.672697</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.124663</td>\n",
       "      <td>0.928478</td>\n",
       "      <td>0.712171</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.974352</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.683799</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.833216</td>\n",
       "      <td>0.967838</td>\n",
       "      <td>0.701069</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.738042</td>\n",
       "      <td>0.933827</td>\n",
       "      <td>0.707648</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.665164</td>\n",
       "      <td>1.042237</td>\n",
       "      <td>0.686678</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.613833</td>\n",
       "      <td>1.064348</td>\n",
       "      <td>0.679276</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.532933</td>\n",
       "      <td>1.271561</td>\n",
       "      <td>0.669819</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.452864</td>\n",
       "      <td>1.345636</td>\n",
       "      <td>0.556743</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.362946</td>\n",
       "      <td>1.462589</td>\n",
       "      <td>0.702714</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.311974</td>\n",
       "      <td>1.323200</td>\n",
       "      <td>0.680921</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.268478</td>\n",
       "      <td>1.512376</td>\n",
       "      <td>0.694490</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.257810</td>\n",
       "      <td>1.499992</td>\n",
       "      <td>0.671875</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.197436</td>\n",
       "      <td>1.416777</td>\n",
       "      <td>0.686266</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.154968</td>\n",
       "      <td>1.600856</td>\n",
       "      <td>0.699013</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.164412</td>\n",
       "      <td>1.648382</td>\n",
       "      <td>0.678865</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.157038</td>\n",
       "      <td>1.558723</td>\n",
       "      <td>0.665296</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.120978</td>\n",
       "      <td>1.759695</td>\n",
       "      <td>0.687911</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.100463</td>\n",
       "      <td>1.755230</td>\n",
       "      <td>0.705592</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.079039</td>\n",
       "      <td>1.626215</td>\n",
       "      <td>0.687089</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.058136</td>\n",
       "      <td>1.710042</td>\n",
       "      <td>0.696546</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.050182</td>\n",
       "      <td>1.710428</td>\n",
       "      <td>0.706826</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.055660</td>\n",
       "      <td>1.817197</td>\n",
       "      <td>0.682155</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.048783</td>\n",
       "      <td>1.806616</td>\n",
       "      <td>0.717105</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.039892</td>\n",
       "      <td>1.853099</td>\n",
       "      <td>0.689145</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.029799</td>\n",
       "      <td>1.749075</td>\n",
       "      <td>0.692434</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.029454</td>\n",
       "      <td>1.920179</td>\n",
       "      <td>0.712993</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.029567</td>\n",
       "      <td>1.988342</td>\n",
       "      <td>0.670230</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.022381</td>\n",
       "      <td>1.872381</td>\n",
       "      <td>0.696957</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.024617</td>\n",
       "      <td>1.889388</td>\n",
       "      <td>0.695724</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.020852</td>\n",
       "      <td>1.827237</td>\n",
       "      <td>0.702714</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.014527</td>\n",
       "      <td>1.804107</td>\n",
       "      <td>0.714227</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.008425</td>\n",
       "      <td>1.838804</td>\n",
       "      <td>0.715049</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.004632</td>\n",
       "      <td>1.879711</td>\n",
       "      <td>0.720806</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.002590</td>\n",
       "      <td>1.829365</td>\n",
       "      <td>0.715049</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>1.831386</td>\n",
       "      <td>0.718339</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.001098</td>\n",
       "      <td>1.828893</td>\n",
       "      <td>0.717105</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.000804</td>\n",
       "      <td>1.878065</td>\n",
       "      <td>0.720395</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.000726</td>\n",
       "      <td>1.855427</td>\n",
       "      <td>0.722862</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>1.843489</td>\n",
       "      <td>0.719161</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>1.874552</td>\n",
       "      <td>0.723684</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>1.855472</td>\n",
       "      <td>0.722862</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>1.841982</td>\n",
       "      <td>0.720395</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>1.851581</td>\n",
       "      <td>0.720806</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>1.881997</td>\n",
       "      <td>0.722862</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>1.863412</td>\n",
       "      <td>0.722862</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>1.874008</td>\n",
       "      <td>0.721217</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.727 +/- 0.006 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# self-supervised: train with 100% labels\n",
    "n_epochs = 50\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls100, InceptionTimePlus, pretrained=True, weights_path=f'data/MVP/{dsid}_200.pth', metrics=accuracy)\n",
    "    learn.fit_one_cycle(n_epochs, 1e-2)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.734 +/- 0.005\n",
      "weights from data/TSBERT/LSST_200.pth successfully transferred!\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.359654</td>\n",
       "      <td>1.954223</td>\n",
       "      <td>0.485609</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.773933</td>\n",
       "      <td>1.174560</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.359023</td>\n",
       "      <td>1.001717</td>\n",
       "      <td>0.679276</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.110271</td>\n",
       "      <td>1.059103</td>\n",
       "      <td>0.649260</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.943131</td>\n",
       "      <td>0.993950</td>\n",
       "      <td>0.697780</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.820002</td>\n",
       "      <td>1.085328</td>\n",
       "      <td>0.638980</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.703817</td>\n",
       "      <td>0.943418</td>\n",
       "      <td>0.700658</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.578584</td>\n",
       "      <td>1.073182</td>\n",
       "      <td>0.716694</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.472466</td>\n",
       "      <td>1.010708</td>\n",
       "      <td>0.705181</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.370321</td>\n",
       "      <td>1.294471</td>\n",
       "      <td>0.700658</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.298935</td>\n",
       "      <td>1.178980</td>\n",
       "      <td>0.729030</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.205274</td>\n",
       "      <td>1.116934</td>\n",
       "      <td>0.725329</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.134245</td>\n",
       "      <td>1.242503</td>\n",
       "      <td>0.727385</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.087392</td>\n",
       "      <td>1.297503</td>\n",
       "      <td>0.721628</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.059800</td>\n",
       "      <td>1.383146</td>\n",
       "      <td>0.722039</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.037657</td>\n",
       "      <td>1.334129</td>\n",
       "      <td>0.738076</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.023080</td>\n",
       "      <td>1.359468</td>\n",
       "      <td>0.734786</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.015836</td>\n",
       "      <td>1.397662</td>\n",
       "      <td>0.733141</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.011492</td>\n",
       "      <td>1.376322</td>\n",
       "      <td>0.735609</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.009538</td>\n",
       "      <td>1.382330</td>\n",
       "      <td>0.731497</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.734 +/- 0.005 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# self-supervised 100% + training\n",
    "n_epochs = 20\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls100, InceptionTimePlus, pretrained=True, weights_path=f'data/MVP/{dsid}_200.pth', metrics=accuracy)\n",
    "    learn.fit_one_cycle(n_epochs, 1e-2)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding data augmentation 🔎🔎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One last thing I'd like to test is the impact of data augmentation when using a pretrained model. \n",
    "\n",
    "I will compare the performance of a model trained from scratch and a pretrained model adding CutMix (CutMix1D in `tsai`). We'll see if the difference in performance still holds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.736 +/- 0.003\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.999574</td>\n",
       "      <td>1.487806</td>\n",
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       "      <td>00:01</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.788427</td>\n",
       "      <td>2.181447</td>\n",
       "      <td>0.338405</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.743320</td>\n",
       "      <td>2.070639</td>\n",
       "      <td>0.356908</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.695977</td>\n",
       "      <td>1.214551</td>\n",
       "      <td>0.597039</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.671329</td>\n",
       "      <td>1.138341</td>\n",
       "      <td>0.631990</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.621758</td>\n",
       "      <td>1.237332</td>\n",
       "      <td>0.601562</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.594546</td>\n",
       "      <td>1.100912</td>\n",
       "      <td>0.645970</td>\n",
       "      <td>00:01</td>\n",
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       "      <td>7</td>\n",
       "      <td>1.545951</td>\n",
       "      <td>1.109454</td>\n",
       "      <td>0.633224</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.532146</td>\n",
       "      <td>1.032417</td>\n",
       "      <td>0.664474</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.527898</td>\n",
       "      <td>1.011768</td>\n",
       "      <td>0.664062</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
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       "      <td>0.989224</td>\n",
       "      <td>0.697780</td>\n",
       "      <td>00:01</td>\n",
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       "      <td>11</td>\n",
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       "      <td>0.943555</td>\n",
       "      <td>0.709704</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.455313</td>\n",
       "      <td>0.952527</td>\n",
       "      <td>0.703947</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.445703</td>\n",
       "      <td>0.911152</td>\n",
       "      <td>0.714227</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.418540</td>\n",
       "      <td>0.883091</td>\n",
       "      <td>0.720395</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.397481</td>\n",
       "      <td>0.902326</td>\n",
       "      <td>0.730263</td>\n",
       "      <td>00:01</td>\n",
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       "      <td>16</td>\n",
       "      <td>1.375082</td>\n",
       "      <td>0.864577</td>\n",
       "      <td>0.736020</td>\n",
       "      <td>00:01</td>\n",
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       "      <td>1.341904</td>\n",
       "      <td>0.854354</td>\n",
       "      <td>0.740132</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.332702</td>\n",
       "      <td>0.856487</td>\n",
       "      <td>0.738898</td>\n",
       "      <td>00:01</td>\n",
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       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.309347</td>\n",
       "      <td>0.850561</td>\n",
       "      <td>0.741776</td>\n",
       "      <td>00:01</td>\n",
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.737 +/- 0.004 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# self-supervised 100% + training\n",
    "n_epochs = 20\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls100, InceptionTimePlus, metrics=accuracy, cbs=CutMix1D())\n",
    "    learn.fit_one_cycle(n_epochs, 1e-2)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/10 accuracy: 0.751 +/- 0.006\n",
      "weights from data/TSBERT/LSST_200.pth successfully transferred!\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.396161</td>\n",
       "      <td>2.052565</td>\n",
       "      <td>0.409951</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.068658</td>\n",
       "      <td>1.457705</td>\n",
       "      <td>0.522204</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.858416</td>\n",
       "      <td>1.213576</td>\n",
       "      <td>0.612253</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.724560</td>\n",
       "      <td>1.155581</td>\n",
       "      <td>0.655016</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.663071</td>\n",
       "      <td>1.171157</td>\n",
       "      <td>0.606908</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.598847</td>\n",
       "      <td>0.989038</td>\n",
       "      <td>0.697368</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.545986</td>\n",
       "      <td>0.983985</td>\n",
       "      <td>0.689556</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.492254</td>\n",
       "      <td>1.001970</td>\n",
       "      <td>0.694490</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.456070</td>\n",
       "      <td>0.948733</td>\n",
       "      <td>0.714227</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.462146</td>\n",
       "      <td>0.916122</td>\n",
       "      <td>0.727796</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.420253</td>\n",
       "      <td>0.913320</td>\n",
       "      <td>0.712582</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.406735</td>\n",
       "      <td>0.875176</td>\n",
       "      <td>0.723684</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.382903</td>\n",
       "      <td>0.863950</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.343559</td>\n",
       "      <td>0.866541</td>\n",
       "      <td>0.737253</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.321192</td>\n",
       "      <td>0.849864</td>\n",
       "      <td>0.730674</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.302497</td>\n",
       "      <td>0.840779</td>\n",
       "      <td>0.742599</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.269869</td>\n",
       "      <td>0.837071</td>\n",
       "      <td>0.744655</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.245579</td>\n",
       "      <td>0.821551</td>\n",
       "      <td>0.746299</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.231015</td>\n",
       "      <td>0.820445</td>\n",
       "      <td>0.747533</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.258627</td>\n",
       "      <td>0.824034</td>\n",
       "      <td>0.744655</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "accuracy: 0.751 +/- 0.006 in 10 tests\n"
     ]
    }
   ],
   "source": [
    "# self-supervised 100% + training with cutmix\n",
    "n_epochs = 20\n",
    "n_tests = 10\n",
    "_result = []\n",
    "for i in range(n_tests):\n",
    "    clear_output()\n",
    "    if i > 0: print(f'{i}/{n_tests} accuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f}')\n",
    "    else: print(f'{i}/{n_tests}')\n",
    "    learn = ts_learner(dls100, InceptionTimePlus, pretrained=True, weights_path=f'data/MVP/{dsid}_200.pth', metrics=accuracy, cbs=CutMix1D())\n",
    "    learn.fit_one_cycle(n_epochs, 1e-2)\n",
    "    _result.append(learn.recorder.values[-1][-1])\n",
    "learn.plot_metrics()\n",
    "print(f'\\naccuracy: {np.mean(_result):.3f} +/- {np.std(_result):.3f} in {n_tests} tests')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see CutMix improves performance in both cases, but the pretrained model still performs better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`MVP` is the first self-supervised method added to the `tsai` library. And it seems to work pretty well. It shows something really interesting: self-supervised learning may improve performance, with or without additional unlabeled data.\n",
    "\n",
    "In this notebook we've demonstrated how easy it is to use a self-supervised method in 2 steps: \n",
    "\n",
    "1. pretrain an architecture using the `MVP` callback.\n",
    "2. fine-tune or train the same architecture using the pretrained model weights.\n",
    "\n",
    "In all cases, performance has been better when using the pretrained model weights as a starting point. \n",
    "\n",
    "In this case, training has proven to be superior to fine-tuning. However, this will not always be the case. It's difficult to know a priori whether fine/tuning or training will provide better results. I'd recommend trying both approaches. \n",
    "\n",
    "In the case of using data augmentation (data augmentation), we've also seen that the pretrained model performs better than the one trained from scratch.\n",
    "\n",
    "`MVP` has shown it can improve performance with a low number of labels (10%) as well as with all labels (100%). \n",
    "\n",
    "I'd encourage you to use `MVP` with your own datasets and share your experience."
   ]
  }
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